def cleanEventTree(experiment):
tree = MultiCactusTree(experiment.getTree())
tree.nameUnlabeledInternalNodes()
for node in tree.breadthFirstTraversal():
if tree.hasName(node):
name = tree.getName(node)
if '.' in name:
newName = name.replace('.', '_')
sys.stderr.write('WARNING renaming event %s to %s\n' %(name, newName))
tree.setName(node, newName)
name = newName
parent = tree.getParent(node)
if parent is not None:
weight = tree.getWeight(parent, node)
if weight is None:
raise RuntimeError('Missing branch length in species_tree tree')
redoPrefix = True
newSuffix = 0
while redoPrefix is True:
redoPrefix = False
for node1 in tree.breadthFirstTraversal():
name1 = tree.getName(node1)
for node2 in tree.breadthFirstTraversal():
name2 = tree.getName(node2)
if node1 != node2 and name1 == name2:
newName = "%s%i" % (name2, newSuffix)
newSuffix += 1
tree.setName(node2, newName)
sys.stderr.write('WARNING renaming event %s to %s\n' % (
name2, newName))
redoPrefix = True
experiment.xmlRoot.attrib["species_tree"] = NXNewick().writeString(tree)
experiment.seqMap = experiment.buildSequenceMap()
def cleanEventTree(experiment):
tree = MultiCactusTree(experiment.getTree())
tree.nameUnlabeledInternalNodes()
for node in tree.breadthFirstTraversal():
if tree.hasName(node):
name = tree.getName(node)
if '.' in name:
newName = name.replace('.', '_')
sys.stderr.write('WARNING renaming event %s to %s\n' %(name, newName))
tree.setName(node, newName)
name = newName
parent = tree.getParent(node)
if parent is not None:
weight = tree.getWeight(parent, node)
if weight is None:
raise RuntimeError('Missing branch length in species_tree tree')
redoPrefix = True
newSuffix = 0
while redoPrefix is True:
redoPrefix = False
for node1 in tree.breadthFirstTraversal():
name1 = tree.getName(node1)
for node2 in tree.breadthFirstTraversal():
name2 = tree.getName(node2)
if node1 != node2 and name1 == name2:
newName = "%s%i" % (name2, newSuffix)
newSuffix += 1
tree.setName(node2, newName)
sys.stderr.write('WARNING renaming event %s to %s\n' % (
name2, newName))
redoPrefix = True
experiment.xmlRoot.attrib["species_tree"] = NXNewick().writeString(tree)
experiment.seqMap = experiment.buildSequenceMap()
def createMCProject(tree, experiment, config, options):
"""
Creates a properly initialized MultiCactusProject.
TODO: This should really all be in the constructor for MultiCactusProject.
"""
mcTree = MultiCactusTree(tree)
mcTree.nameUnlabeledInternalNodes(config.getDefaultInternalNodePrefix())
mcTree.computeSubtreeRoots()
mcProj = MultiCactusProject()
for genome in experiment.getGenomesWithSequence():
mcProj.inputSequenceMap[genome] = experiment.getSequenceID(genome)
mcProj.mcTree = mcTree
if config.getDoSelfAlignment():
mcTree.addSelfEdges()
for name in mcProj.mcTree.getSubtreeRootNames():
expPath = "%s/%s/%s_experiment.xml" % (options.path, name, name)
mcProj.expMap[name] = os.path.abspath(expPath)
alignmentRootId = mcProj.mcTree.getRootId()
if options.root is not None:
try:
alignmentRootId = mcProj.mcTree.getNodeId(options.root)
except:
raise RuntimeError("Specified root name %s not found in tree" %
options.root)
fillInOutgroups(mcProj, options.outgroupNames, config, alignmentRootId)
# if necessary, we reroot the tree at the specified alignment root id. all leaf genomes
# that are no longer in the tree, but still used as outgroups, are moved into special fields
# so that we can remember to, say, get their paths for preprocessing.
specifyAlignmentRoot(mcProj, experiment, alignmentRootId)
return mcProj
def testAddSelf(self):
trueSelf = '((((((((HUMAN:0.006969)HUMAN_self:0.006969,(CHIMP:0.009727)CHIMP_self:0.009727)Anc7:0.025291)Anc7_self:0.025291,(BABOON:0.044568)BABOON_self:0.044568)Anc3:0.11)Anc3_self:0.11,(((MOUSE:0.072818)MOUSE_self:0.072818,(RAT:0.081244)RAT_self:0.081244)Anc4:0.260342)Anc4_self:0.260342)Anc1:0.02326)Anc1_self:0.02326,(((((DOG:0.07)DOG_self:0.07,(CAT:0.07)CAT_self:0.07)Anc5:0.087381)Anc5_self:0.087381,(((PIG:0.06)PIG_self:0.06,(COW:0.06)COW_self:0.06)Anc6:0.104728)Anc6_self:0.104728)Anc2:0.04)Anc2_self:0.04)Anc0;'
tree = MultiCactusTree(self.mcTree1)
tree.nameUnlabeledInternalNodes()
tree.computeSubtreeRoots()
tree.addSelfEdges()
treeString = NXNewick().writeString(tree)
self.assertEqual(treeString, trueSelf)
def testAddSelf(self):
trueSelf = "((((((((HUMAN:0.006969)HUMAN_self:0.006969,(CHIMP:0.009727)CHIMP_self:0.009727)Anc7:0.025291)Anc7_self:0.025291,(BABOON:0.044568)BABOON_self:0.044568)Anc3:0.11)Anc3_self:0.11,(((MOUSE:0.072818)MOUSE_self:0.072818,(RAT:0.081244)RAT_self:0.081244)Anc4:0.260342)Anc4_self:0.260342)Anc1:0.02326)Anc1_self:0.02326,(((((DOG:0.07)DOG_self:0.07,(CAT:0.07)CAT_self:0.07)Anc5:0.087381)Anc5_self:0.087381,(((PIG:0.06)PIG_self:0.06,(COW:0.06)COW_self:0.06)Anc6:0.104728)Anc6_self:0.104728)Anc2:0.04)Anc2_self:0.04)Anc0;"
tree = MultiCactusTree(self.mcTree1)
tree.nameUnlabeledInternalNodes()
tree.computeSubtreeRoots()
tree.addSelfEdges()
treeString = NXNewick().writeString(tree)
self.assertEqual(treeString, trueSelf)
def setUp(self):
unittest.TestCase.setUp(self)
self.trees = randomTreeSet()
self.mcTrees = []
self.tempDir = getTempDirectory(os.getcwd())
self.tempFa = os.path.join(self.tempDir, "seq.fa")
with open(self.tempFa, "w") as f:
f.write(">temp\nNNNNNNNCNNNNAAAAAAAAAAAAAAANNNNNNN\n")
self.dummySeqMaps = []
for tree in self.trees:
if tree.size() < 50:
mcTree = MultiCactusTree(tree)
seqMap = dict()
for i in mcTree.breadthFirstTraversal():
mcTree.setName(i, "Node%s" % str(i))
seqMap["Node%s" % str(i)] = self.tempFa
mcTree.computeSubtreeRoots()
mcTree.nameUnlabeledInternalNodes()
self.mcTrees.append(mcTree)
self.dummySeqMaps.append(seqMap)
# Boreoeutherian tree
borTree = '((((HUMAN:0.006969,CHIMP:0.009727)Anc7:0.025291,BABOON:0.044568)Anc6:0.11,(MOUSE:0.072818,RAT:0.081244)Anc5:0.260342)Anc4:0.023260,((DOG:0.07,CAT:0.07)Anc3:0.087381,(PIG:0.06,COW:0.06)Anc2:0.104728)Anc1:0.04)Anc0;'
self.borMcTree = MultiCactusTree(NXNewick().parseString(
borTree, addImpliedRoots=False))
self.borMcTree.computeSubtreeRoots()
self.borMcTree.nameUnlabeledInternalNodes()
self.mcTrees.append(self.borMcTree)
# Eutherian backbone tree
backbone = '(((((((((((Homo_sapiens:0.00655,Pan_troglodytes:0.00684):0.00422,Gorilla_gorilla_gorilla:0.008964):0.009693,Pongo_abelii:0.01894):0.015511,Macaca_mulatta:0.043601):0.08444,Aotus_nancymaae:0.08):0.08,Microcebus_murinus:0.10612):0.043494,Galeopterus_variegatus:0.134937):0.04,((((Jaculus_jaculus:0.1,(Microtus_ochrogaster:0.14,(Mus_musculus:0.084509,Rattus_norvegicus:0.091589):0.047773):0.06015):0.122992,(Heterocephalus_glaber:0.1,(Cavia_porcellus:0.065629,(Chinchilla_lanigera:0.06,Octodon_degus:0.1):0.06):0.05):0.06015):0.05,Marmota_marmota:0.1):0.05,Oryctolagus_cuniculus:0.21569):0.04):0.040593,(((Sus_scrofa:0.12,(Orcinus_orca:0.069688,(Bos_taurus:0.04,Capra_hircus:0.04):0.09):0.045488):0.02,((Equus_caballus:0.109397,(Felis_catus:0.098612,(Canis_lupus_familiaris:0.052458,Mustela_putorius_furo:0.08):0.02):0.049845):0.02,(Pteropus_alecto:0.1,Eptesicus_fuscus:0.08):0.033706):0.03):0.025,Erinaceus_europaeus:0.278178):0.021227):0.023664,(((Loxodonta_africana:0.022242,Procavia_capensis:0.145358):0.076687,Chrysochloris_asiatica:0.04):0.05,Dasypus_novemcinctus:0.169809):0.02)backbone_root:0.234728,(Monodelphis_domestica:0.125686,Sarcophilus_harrisii:0.12):0.2151);'
self.backboneTree = MultiCactusTree(NXNewick().parseString(
backbone, addImpliedRoots=False))
self.backboneTree.computeSubtreeRoots()
self.backboneTree.nameUnlabeledInternalNodes()
self.mcTrees.append(self.backboneTree)
seqLens = dict()
seqLens["HUMAN"] = 57553
seqLens["CHIMP"] = 57344
seqLens["BABOON"] = 58960
seqLens["MOUSE"] = 32750
seqLens["RAT"] = 38436
seqLens["DOG"] = 54187
seqLens["CAT"] = 50283
seqLens["PIG"] = 54843
seqLens["COW"] = 55508
self.blanchetteSeqMap = dict()
for event, seqLen in seqLens.items():
p = os.path.join(self.tempDir, event + ".fa")
with open(p, "w") as f:
f.write(">%s\n" % event)
f.write(''.join(['A'] * seqLen))
f.write('\n')
self.blanchetteSeqMap[event] = p
def cactusPrepare(options, project):
""" annotate a SeqFile with ancestral names as well as paths for output sequences."""
# read the input
seqFile = SeqFile(options.seqFile)
configNode = ET.parse(options.configFile).getroot()
config = ConfigWrapper(configNode)
# prepare output sequence directory
# todo: support remote (ie s3) output directory
try:
os.makedirs(options.outSeqDir)
except:
pass
if not os.path.isdir(options.outSeqDir):
raise RuntimeError('Unable to create output sequence directory \'{}\''.format(options.outSeqDir))
if not os.access(options.outSeqDir, os.W_OK):
logger.warning('Output sequence directory is not writeable: \'{}\''.format(options.outSeqDir))
# hack the configfile to skip preprocessing and write it to the output dir
if options.preprocessOnly:
config.removePreprocessors()
options.configFile = os.path.join(options.outSeqDir, 'config.xml')
config.writeXML(options.configFile)
# pass through the config file to the options
# todo (don't like second hard-code check of .xml path)
if options.configFile != os.path.join(cactusRootPath(), "cactus_progressive_config.xml"):
options.cactusOptions += ' --configFile {}'.format(options.configFile)
# get the ancestor names
tree = MultiCactusTree(seqFile.tree)
tree.nameUnlabeledInternalNodes(prefix = config.getDefaultInternalNodePrefix())
# make the output
outSeqFile = SeqFile()
outSeqFile.tree= tree
outSeqFile.pathMap = seqFile.pathMap
outSeqFile.outgroups = seqFile.outgroups
# update paths for preprocessed leaves or inferred ancestors
for node in outSeqFile.tree.breadthFirstTraversal():
name = outSeqFile.tree.getName(node)
leaf = outSeqFile.tree.isLeaf(node)
if leaf or (not leaf and name not in seqFile.pathMap and not options.preprocessOnly):
out_basename = seqFile.pathMap[name] if name in seqFile.pathMap else '{}.fa'.format(name)
outSeqFile.pathMap[name] = os.path.join(options.outSeqDir, os.path.basename(out_basename))
# write the output
with open(options.outSeqFile, 'w') as out_sf:
out_sf.write(str(outSeqFile))
# write the instructions
print(get_plan(options, project, outSeqFile))
def getTree(self, onlyThisSubtree=False):
treeString = self.xmlRoot.attrib["species_tree"]
ret = NXNewick().parseString(treeString, addImpliedRoots = False)
if onlyThisSubtree:
# Get a subtree containing only the reference node and its
# children, rather than a species tree including the
# outgroups as well
multiCactus = MultiCactusTree(ret)
multiCactus.nameUnlabeledInternalNodes()
multiCactus.computeSubtreeRoots()
ret = multiCactus.extractSubTree(self.getReferenceNameFromConfig())
return ret
def getTree(self, onlyThisSubtree=False):
treeString = self.xmlRoot.attrib["species_tree"]
ret = NXNewick().parseString(treeString, addImpliedRoots=False)
if onlyThisSubtree:
# Get a subtree containing only the reference node and its
# children, rather than a species tree including the
# outgroups as well
multiCactus = MultiCactusTree(ret)
multiCactus.nameUnlabeledInternalNodes()
multiCactus.computeSubtreeRoots()
ret = multiCactus.extractSubTree(self.getRootGenome())
return ret
def setUp(self):
unittest.TestCase.setUp(self)
self.trees = randomTreeSet()
self.mcTrees = []
self.tempDir = getTempDirectory(os.getcwd())
self.tempFa = os.path.join(self.tempDir, "seq.fa")
with open(self.tempFa, "w") as f:
f.write(">temp\nNNNNNNNCNNNNAAAAAAAAAAAAAAANNNNNNN\n")
self.dummySeqMaps = []
for tree in self.trees:
if tree.size() < 50:
mcTree = MultiCactusTree(tree, tree.degree())
seqMap = dict()
for i in mcTree.breadthFirstTraversal():
mcTree.setName(i, "Node%s" % str(i))
seqMap["Node%s" % str(i)] = self.tempFa
mcTree.computeSubtreeRoots()
mcTree.nameUnlabeledInternalNodes()
self.mcTrees.append(mcTree)
self.dummySeqMaps.append(seqMap)
# Boreoeutherian tree
borTree = '((((HUMAN:0.006969,CHIMP:0.009727)Anc7:0.025291,BABOON:0.044568)Anc6:0.11,(MOUSE:0.072818,RAT:0.081244)Anc5:0.260342)Anc4:0.023260,((DOG:0.07,CAT:0.07)Anc3:0.087381,(PIG:0.06,COW:0.06)Anc2:0.104728)Anc1:0.04)Anc0;'
self.borMcTree = MultiCactusTree(NXNewick().parseString(borTree, addImpliedRoots=False))
self.borMcTree.computeSubtreeRoots()
self.borMcTree.nameUnlabeledInternalNodes()
self.mcTrees.append(self.borMcTree)
# Eutherian backbone tree
backbone = '(((((((((((Homo_sapiens:0.00655,Pan_troglodytes:0.00684):0.00422,Gorilla_gorilla_gorilla:0.008964):0.009693,Pongo_abelii:0.01894):0.015511,Macaca_mulatta:0.043601):0.08444,Aotus_nancymaae:0.08):0.08,Microcebus_murinus:0.10612):0.043494,Galeopterus_variegatus:0.134937):0.04,((((Jaculus_jaculus:0.1,(Microtus_ochrogaster:0.14,(Mus_musculus:0.084509,Rattus_norvegicus:0.091589):0.047773):0.06015):0.122992,(Heterocephalus_glaber:0.1,(Cavia_porcellus:0.065629,(Chinchilla_lanigera:0.06,Octodon_degus:0.1):0.06):0.05):0.06015):0.05,Marmota_marmota:0.1):0.05,Oryctolagus_cuniculus:0.21569):0.04):0.040593,(((Sus_scrofa:0.12,(Orcinus_orca:0.069688,(Bos_taurus:0.04,Capra_hircus:0.04):0.09):0.045488):0.02,((Equus_caballus:0.109397,(Felis_catus:0.098612,(Canis_lupus_familiaris:0.052458,Mustela_putorius_furo:0.08):0.02):0.049845):0.02,(Pteropus_alecto:0.1,Eptesicus_fuscus:0.08):0.033706):0.03):0.025,Erinaceus_europaeus:0.278178):0.021227):0.023664,(((Loxodonta_africana:0.022242,Procavia_capensis:0.145358):0.076687,Chrysochloris_asiatica:0.04):0.05,Dasypus_novemcinctus:0.169809):0.02)backbone_root:0.234728,(Monodelphis_domestica:0.125686,Sarcophilus_harrisii:0.12):0.2151);'
self.backboneTree = MultiCactusTree(NXNewick().parseString(backbone, addImpliedRoots=False))
self.backboneTree.computeSubtreeRoots()
self.backboneTree.nameUnlabeledInternalNodes()
self.mcTrees.append(self.backboneTree)
seqLens = dict()
seqLens["HUMAN"] = 57553
seqLens["CHIMP"] = 57344
seqLens["BABOON"] = 58960
seqLens["MOUSE"] = 32750
seqLens["RAT"] = 38436
seqLens["DOG"] = 54187
seqLens["CAT"] = 50283
seqLens["PIG"] = 54843
seqLens["COW"] = 55508
self.blanchetteSeqMap = dict()
for event, seqLen in seqLens.items():
p = os.path.join(self.tempDir, event +".fa")
with open(p, "w") as f:
f.write(">%s\n" % event)
f.write(''.join(['A'] * seqLen))
f.write('\n')
self.blanchetteSeqMap[event] = p
def testAddOutgroup(self):
trueOg = '((((HUMAN:0.006969,CHIMP:0.009727)Anc7:0.025291,BABOON:0.044568)Anc3:0.11,(MOUSE:0.072818,RAT:0.081244)Anc4:0.260342)Anc1:0.02326,((DOG:0.07,CAT:0.07)Anc5:0.087381,(PIG:0.06,COW:0.06)Anc6:0.104728)Anc2:0.04,outgroup:1.7)Anc0;'
tree = MultiCactusTree(self.mcTree1)
tree.nameUnlabeledInternalNodes()
tree.computeSubtreeRoots()
tree.addOutgroup("outgroup", 1.7)
treeString = NXNewick().writeString(tree)
self.assertEqual(treeString, trueOg)
trueLeafOg = "(A:1.1,outgroup:1.1);"
leafTreeString = "A;"
parser = NXNewick()
leafTree = MultiCactusTree(parser.parseString(leafTreeString, addImpliedRoots = False))
leafTree.nameUnlabeledInternalNodes()
leafTree.computeSubtreeRoots()
leafTree.addOutgroup("outgroup", 2.2)
leafTreeOutString = NXNewick().writeString(leafTree)
self.assertEqual(leafTreeOutString, trueLeafOg)
def testAddOutgroup(self):
trueOg = "((((HUMAN:0.006969,CHIMP:0.009727)Anc7:0.025291,BABOON:0.044568)Anc3:0.11,(MOUSE:0.072818,RAT:0.081244)Anc4:0.260342)Anc1:0.02326,((DOG:0.07,CAT:0.07)Anc5:0.087381,(PIG:0.06,COW:0.06)Anc6:0.104728)Anc2:0.04,outgroup:1.7)Anc0;"
tree = MultiCactusTree(self.mcTree1)
tree.nameUnlabeledInternalNodes()
tree.computeSubtreeRoots()
tree.addOutgroup("outgroup", 1.7)
treeString = NXNewick().writeString(tree)
self.assertEqual(treeString, trueOg)
trueLeafOg = "(A:1.1,outgroup:1.1);"
leafTreeString = "A;"
parser = NXNewick()
leafTree = MultiCactusTree(parser.parseString(leafTreeString, addImpliedRoots=False))
leafTree.nameUnlabeledInternalNodes()
leafTree.computeSubtreeRoots()
leafTree.addOutgroup("outgroup", 2.2)
leafTreeOutString = NXNewick().writeString(leafTree)
self.assertEqual(leafTreeOutString, trueLeafOg)
def runCactusAfterBlastOnly(options):
with Toil(options) as toil:
importSingularityImage(options)
#Run the workflow
if options.restart:
halID = toil.restart()
else:
options.cactusDir = getTempDirectory()
# apply path overrides. this was necessary for wdl which doesn't take kindly to
# text files of local paths (ie seqfile). one way to fix would be to add support
# for s3 paths and force wdl to use it. a better way would be a more fundamental
# interface shift away from files of paths throughout all of cactus
if options.pathOverrides:
seqFile = SeqFile(options.seqFile)
configNode = ET.parse(options.configFile).getroot()
config = ConfigWrapper(configNode)
tree = MultiCactusTree(seqFile.tree)
tree.nameUnlabeledInternalNodes(
prefix=config.getDefaultInternalNodePrefix())
for name, override in zip(options.pathOverrideNames,
options.pathOverrides):
seqFile.pathMap[name] = override
override_seq = os.path.join(options.cactusDir,
'seqFile.override')
with open(override_seq, 'w') as out_sf:
out_sf.write(str(seqFile))
options.seqFile = override_seq
#to be consistent with all-in-one cactus, we make sure the project
#isn't limiting itself to the subtree (todo: parameterize so root can
#be passed through from prepare to blast/align)
proj_options = copy.deepcopy(options)
proj_options.root = None
#Create the progressive cactus project (as we do in runCactusProgressive)
projWrapper = ProjectWrapper(proj_options,
proj_options.configFile,
ignoreSeqPaths=options.root)
projWrapper.writeXml()
pjPath = os.path.join(
options.cactusDir, ProjectWrapper.alignmentDirName,
'%s_project.xml' % ProjectWrapper.alignmentDirName)
assert os.path.exists(pjPath)
project = MultiCactusProject()
if not os.path.isdir(options.cactusDir):
os.makedirs(options.cactusDir)
project.readXML(pjPath)
# open up the experiment (as we do in ProgressiveUp.run)
# note that we copy the path into the options here
experimentFile = project.expMap[options.root]
expXml = ET.parse(experimentFile).getroot()
experiment = ExperimentWrapper(expXml)
configPath = experiment.getConfigPath()
configXml = ET.parse(configPath).getroot()
seqIDMap = dict()
tree = MultiCactusTree(experiment.getTree()).extractSubTree(
options.root)
leaves = [tree.getName(leaf) for leaf in tree.getLeaves()]
outgroups = experiment.getOutgroupGenomes()
genome_set = set(leaves + outgroups)
# this is a hack to allow specifying all the input on the command line, rather than using suffix lookups
def get_input_path(suffix=''):
base_path = options.cigarsFile[0]
for input_path in options.cigarsFile:
if suffix and input_path.endswith(suffix):
return input_path
if os.path.basename(base_path).startswith(
os.path.basename(input_path)):
base_path = input_path
return base_path + suffix
# import the outgroups
outgroupIDs = []
outgroup_fragment_found = False
for i, outgroup in enumerate(outgroups):
try:
outgroupID = toil.importFile(
makeURL(get_input_path('.og_fragment_{}'.format(i))))
outgroupIDs.append(outgroupID)
experiment.setSequenceID(outgroup, outgroupID)
outgroup_fragment_found = True
assert not options.pangenome
except:
# we assume that input is not coming from cactus blast, so we'll treat output
# sequences normally and not go looking for fragments
outgroupIDs = []
break
#import the sequences (that we need to align for the given event, ie leaves and outgroups)
for genome, seq in list(project.inputSequenceMap.items()):
if genome in leaves or (not outgroup_fragment_found
and genome in outgroups):
if os.path.isdir(seq):
tmpSeq = getTempFile()
catFiles([
os.path.join(seq, subSeq)
for subSeq in os.listdir(seq)
], tmpSeq)
seq = tmpSeq
seq = makeURL(seq)
experiment.setSequenceID(genome, toil.importFile(seq))
if not outgroup_fragment_found:
outgroupIDs = [
experiment.getSequenceID(outgroup)
for outgroup in outgroups
]
# write back the experiment, as CactusWorkflowArguments wants a path
experiment.writeXML(experimentFile)
#import cactus config
if options.configFile:
cactusConfigID = toil.importFile(makeURL(options.configFile))
else:
cactusConfigID = toil.importFile(
makeURL(project.getConfigPath()))
project.setConfigID(cactusConfigID)
project.syncToFileStore(toil)
configNode = ET.parse(project.getConfigPath()).getroot()
configWrapper = ConfigWrapper(configNode)
configWrapper.substituteAllPredefinedConstantsWithLiterals()
if options.pangenome:
# turn off the megablock filter as it ruins non-all-to-all alignments
configWrapper.disableCafMegablockFilter()
# the recoverable chains parameter does not seem to play nicely with star-like alignments either
#configWrapper.disableRecoverableChains()
workFlowArgs = CactusWorkflowArguments(
options,
experimentFile=experimentFile,
configNode=configNode,
seqIDMap=project.inputSequenceIDMap)
#import the files that cactus-blast made
workFlowArgs.alignmentsID = toil.importFile(
makeURL(get_input_path()))
workFlowArgs.secondaryAlignmentsID = None
if not options.pafInput:
try:
workFlowArgs.secondaryAlignmentsID = toil.importFile(
makeURL(get_input_path('.secondary')))
except:
pass
workFlowArgs.outgroupFragmentIDs = outgroupIDs
workFlowArgs.ingroupCoverageIDs = []
if outgroup_fragment_found and len(outgroups) > 0:
for i in range(len(leaves)):
workFlowArgs.ingroupCoverageIDs.append(
toil.importFile(
makeURL(get_input_path(
'.ig_coverage_{}'.format(i)))))
halID = toil.start(
Job.wrapJobFn(run_cactus_align,
configWrapper,
workFlowArgs,
project,
doRenaming=options.nonCactusInput,
pafInput=options.pafInput))
# export the hal
toil.exportFile(halID, makeURL(options.outputHal))
def createMCProject(tree, experiment, config, options):
mcTree = MultiCactusTree(tree, config.getSubtreeSize())
mcTree.nameUnlabeledInternalNodes(config.getDefaultInternalNodePrefix())
mcTree.computeSubtreeRoots()
mcProj = MultiCactusProject()
mcProj.mcTree = mcTree
mcProj.inputSequences = experiment.getSequences()[:]
mcProj.outputSequenceDir = experiment.getOutputSequenceDir()
if config.getDoSelfAlignment():
mcTree.addSelfEdges()
for name in mcProj.mcTree.getSubtreeRootNames():
expPath = "%s/%s/%s_experiment.xml" % (options.path, name, name)
mcProj.expMap[name] = os.path.abspath(expPath)
alignmentRootId = mcProj.mcTree.getRootId()
if options.root is not None:
try:
alignmentRootId = mcProj.mcTree.getNodeId(options.root)
except Exception as e:
raise RuntimeError("Specified root name %s not found in tree" % options.root)
mcProj.outgroup = None
if config.getOutgroupStrategy() == 'greedy':
# use the provided outgroup candidates, or use all outgroups
# as candidates if none are given
mcProj.outgroup = GreedyOutgroup()
mcProj.outgroup.importTree(mcProj.mcTree, alignmentRootId)
mcProj.outgroup.greedy(threshold=config.getOutgroupThreshold(),
candidateSet=options.outgroupNames,
candidateChildFrac=config.getOutgroupAncestorQualityFraction(),
maxNumOutgroups=config.getMaxNumOutgroups())
elif config.getOutgroupStrategy() == 'greedyLeaves':
# use all leaves as outgroups, unless outgroup candidates are given
mcProj.outgroup = GreedyOutgroup()
mcProj.outgroup.importTree(mcProj.mcTree, alignmentRootId)
ogSet = options.outgroupNames
if ogSet is None:
ogSet = set([mcProj.mcTree.getName(x) for x in mcProj.mcTree.getLeaves()])
mcProj.outgroup.greedy(threshold=config.getOutgroupThreshold(),
candidateSet=ogSet,
candidateChildFrac=2.0,
maxNumOutgroups=config.getMaxNumOutgroups())
elif config.getOutgroupStrategy() == 'greedyPreference':
# prefer the provided outgroup candidates, if any, but use
# other nodes as "filler" if we can't find enough.
mcProj.outgroup = GreedyOutgroup()
mcProj.outgroup.importTree(mcProj.mcTree, alignmentRootId)
mcProj.outgroup.greedy(threshold=config.getOutgroupThreshold(),
candidateSet=options.outgroupNames,
candidateChildFrac=config.getOutgroupAncestorQualityFraction(),
maxNumOutgroups=config.getMaxNumOutgroups())
mcProj.outgroup.greedy(threshold=config.getOutgroupThreshold(),
candidateSet=None,
candidateChildFrac=config.getOutgroupAncestorQualityFraction(),
maxNumOutgroups=config.getMaxNumOutgroups())
elif config.getOutgroupStrategy() == 'dynamic':
# dynamic programming algorithm that exactly optimizes probability
# that base in target node aligns to at least one base in the
# outgroup set. Caveats are that it only returns leaves, and
# the model used for optimization is super naive. Still, it does
# some things better than greedy approaches such as properly account
# for phylogenetic redundancy, as well as try to factor assembly
# size/quality automatically.
mcProj.outgroup = DynamicOutgroup()
mcProj.outgroup.importTree(mcProj.mcTree, mcProj.getInputSequenceMap(), alignmentRootId,
candidateSet=options.outgroupNames)
mcProj.outgroup.compute(maxNumOutgroups=config.getMaxNumOutgroups())
elif config.getOutgroupStrategy() != 'none':
raise RuntimeError("Could not understand outgroup strategy %s" % config.getOutgroupStrategy())
# if necessary, we reroot the tree at the specified alignment root id. all leaf genomes
# that are no longer in the tree, but still used as outgroups, are moved into special fields
# so that we can remember to, say, get their paths for preprocessing.
specifyAlignmentRoot(mcProj, alignmentRootId)
return mcProj
class TestCase(unittest.TestCase):
def setUp(self):
unittest.TestCase.setUp(self)
self.mcTree1 = None
self.mcTree1a = None
self.mcTree2 = None
self.__generateTrees()
def testSanity(self):
parser = NXNewick()
mcTree1 = MultiCactusTree(parser.parseString(self.tree1, addImpliedRoots=False))
tree1String = NXNewick().writeString(mcTree1)
self.assertEqual(tree1String, self.tree1)
mcTree2 = MultiCactusTree(parser.parseString(self.tree2, addImpliedRoots=False), subtreeSize=3)
tree2String = NXNewick().writeString(mcTree2)
self.assertEqual(tree2String, self.tree2)
def testSubtrees(self):
roots1 = ["Anc0", "Anc1", "Anc2", "Anc3", "Anc4", "Anc5", "Anc6", "Anc7"]
roots1a = ["Anc0", "Anc3", "Anc4", "Anc5", "Anc6"]
roots2 = ["Anc0", "Anc1", "Anc2", "Anc3", "Anc4"]
subTree1_a3 = "(Anc7:0.025291,BABOON:0.044568)Anc3;"
subTree1a_a0 = "((Anc3:0.11,Anc4:0.260342)Anc1:0.02326,(Anc5:0.087381,Anc6:0.104728)Anc2:0.04)Anc0;"
subTree2_a3 = "(monkey:100.8593,cat:47.14069)Anc5;"
trueRoots = [roots1, roots1a, roots2]
trueSubtrees = [subTree1_a3, subTree1a_a0, subTree2_a3]
trees = [self.mcTree1, self.mcTree1a, self.mcTree2]
ancs = ["Anc3", "Anc0", "Anc5"]
for i in range(0, 3):
roots = trees[i].getSubtreeRootNames()
self.assertEqual(sorted(roots), sorted(trueRoots[i]))
subtree = trees[i].extractSubTree(ancs[i])
subtree = NXNewick().writeString(subtree)
self.assertEqual(subtree, trueSubtrees[i])
def testAddSelf(self):
trueSelf = "((((((((HUMAN:0.006969)HUMAN_self:0.006969,(CHIMP:0.009727)CHIMP_self:0.009727)Anc7:0.025291)Anc7_self:0.025291,(BABOON:0.044568)BABOON_self:0.044568)Anc3:0.11)Anc3_self:0.11,(((MOUSE:0.072818)MOUSE_self:0.072818,(RAT:0.081244)RAT_self:0.081244)Anc4:0.260342)Anc4_self:0.260342)Anc1:0.02326)Anc1_self:0.02326,(((((DOG:0.07)DOG_self:0.07,(CAT:0.07)CAT_self:0.07)Anc5:0.087381)Anc5_self:0.087381,(((PIG:0.06)PIG_self:0.06,(COW:0.06)COW_self:0.06)Anc6:0.104728)Anc6_self:0.104728)Anc2:0.04)Anc2_self:0.04)Anc0;"
tree = MultiCactusTree(self.mcTree1)
tree.nameUnlabeledInternalNodes()
tree.computeSubtreeRoots()
tree.addSelfEdges()
treeString = NXNewick().writeString(tree)
self.assertEqual(treeString, trueSelf)
def testAddOutgroup(self):
trueOg = "((((HUMAN:0.006969,CHIMP:0.009727)Anc7:0.025291,BABOON:0.044568)Anc3:0.11,(MOUSE:0.072818,RAT:0.081244)Anc4:0.260342)Anc1:0.02326,((DOG:0.07,CAT:0.07)Anc5:0.087381,(PIG:0.06,COW:0.06)Anc6:0.104728)Anc2:0.04,outgroup:1.7)Anc0;"
tree = MultiCactusTree(self.mcTree1)
tree.nameUnlabeledInternalNodes()
tree.computeSubtreeRoots()
tree.addOutgroup("outgroup", 1.7)
treeString = NXNewick().writeString(tree)
self.assertEqual(treeString, trueOg)
trueLeafOg = "(A:1.1,outgroup:1.1);"
leafTreeString = "A;"
parser = NXNewick()
leafTree = MultiCactusTree(parser.parseString(leafTreeString, addImpliedRoots=False))
leafTree.nameUnlabeledInternalNodes()
leafTree.computeSubtreeRoots()
leafTree.addOutgroup("outgroup", 2.2)
leafTreeOutString = NXNewick().writeString(leafTree)
self.assertEqual(leafTreeOutString, trueLeafOg)
def testExtractSpanningTree(self):
"""Tests whether extracting a binary spanning tree works correctly."""
prevNewick1 = NXNewick().writeString(self.mcTree1)
# Check a dead-simple spanning tree with 3 closely related leaves.
spanHCB = self.mcTree1.extractSpanningTree(["HUMAN", "CHIMP", "BABOON"])
# Check that the existing tree hasn't been modified (OK, a bit
# silly, but just in case).
self.assertEqual(NXNewick().writeString(self.mcTree1), prevNewick1)
# Check the actual spanning tree.
self.assertEqual(
NXNewick().writeString(spanHCB), "((HUMAN:0.006969,CHIMP:0.009727)Anc7:0.025291,BABOON:0.044568)Anc3;"
)
# Now test a more complicated tree, where we should remove as
# many of the ancestors as possible (they will add extra
# losses for no reason!).
spanHCC = self.mcTree1.extractSpanningTree(["HUMAN", "CHIMP", "CAT"])
self.assertEqual(NXNewick().writeString(self.mcTree1), prevNewick1)
self.assertEqual(
NXNewick().writeString(spanHCC), "((HUMAN:0.006969,CHIMP:0.009727)Anc7:0.158551,CAT:0.197381)Anc0;"
)
def __generateTrees(self):
self.tree1 = "((((HUMAN:0.006969,CHIMP:0.009727):0.025291,BABOON:0.044568):0.11,(MOUSE:0.072818,RAT:0.081244):0.260342):0.02326,((DOG:0.07,CAT:0.07):0.087381,(PIG:0.06,COW:0.06):0.104728):0.04);"
self.tree2 = "((raccoon:19.19959,bear:6.80041):0.846,((sea_lion:11.997,seal:12.003):7.52973,((monkey:100.8593,cat:47.14069):20.59201,weasel:18.87953):2.0946):3.87382,dog:25.46154);"
parser = NXNewick()
self.mcTree1 = MultiCactusTree(parser.parseString(self.tree1, addImpliedRoots=False))
self.mcTree1a = MultiCactusTree(parser.parseString(self.tree1, addImpliedRoots=False), subtreeSize=4)
self.mcTree2 = MultiCactusTree(parser.parseString(self.tree2, addImpliedRoots=False), subtreeSize=3)
self.mcTree1.nameUnlabeledInternalNodes()
self.mcTree1a.nameUnlabeledInternalNodes()
self.mcTree2.nameUnlabeledInternalNodes()
self.mcTree1.computeSubtreeRoots()
self.mcTree1a.computeSubtreeRoots()
self.mcTree2.computeSubtreeRoots()
def main():
options = get_options()
with Toil(options) as workflow:
setupBinaries(options)
importSingularityImage(options)
## Preprocessing:
if (options.pathOverrides or options.pathOverrideNames):
if not options.pathOverrides or not options.pathOverrideNames or \
len(options.pathOverrideNames) != len(options.pathOverrides):
raise RuntimeError(
'same number of values must be passed to --pathOverrides and --pathOverrideNames'
)
# apply path overrides. this was necessary for wdl which doesn't take kindly to
# text files of local paths (ie seqfile). one way to fix would be to add support
# for s3 paths and force wdl to use it. a better way would be a more fundamental
# interface shift away from files of paths throughout all of cactus
if options.pathOverrides:
seqFile = SeqFile(options.seqFile)
configNode = ET.parse(options.configFile).getroot()
config = ConfigWrapper(configNode)
tree = MultiCactusTree(seqFile.tree)
tree.nameUnlabeledInternalNodes(
prefix=config.getDefaultInternalNodePrefix())
for name, override in zip(options.pathOverrideNames,
options.pathOverrides):
seqFile.pathMap[name] = override
override_seq = os.path.join(options.cactusDir, 'seqFile.override')
with open(override_seq, 'w') as out_sf:
out_sf.write(str(seqFile))
options.seqFile = override_seq
# Import asms; by default, prepends unique IDs in the technique used in cactus-blast.
asms = get_asms_from_seqfile(options.seqFile, workflow)
## Perform alignments:
if not workflow.options.restart:
alignments = workflow.start(
Job.wrapJobFn(run_cactus_reference_align, asms, options.refID,
options.debug_export, options.dipcall_bed_filter,
options.dipcall_vcf_filter))
else:
alignments = workflow.restart()
if options.debug_export:
# first, ensure the debug dir exists.
if not os.path.isdir(options.debug_export_dir):
os.mkdir(options.debug_export_dir)
print(alignments)
# Then return value is: (all_primary, all_secondary, ref_mappings, primary_mappings, secondary_mappings)
for asm, mapping_file in alignments[2].items():
workflow.exportFile(
mapping_file, 'file://' +
os.path.abspath("mappings_for_" + asm + ".paf"))
for asm, mapping_file in alignments[3].items():
workflow.exportFile(
mapping_file, 'file://' +
os.path.abspath("mappings_for_" + asm + ".cigar"))
for asm, mapping_file in alignments[4].items():
workflow.exportFile(
mapping_file, 'file://' +
os.path.abspath("mappings_for_" + asm + ".cigar.secondry"))
## Save alignments:
if options.dipcall_vcf_filter: # this is substantially less restrictive than the dipcall_bed_filter.
dipcall_filtered = workflow.start(
Job.wrapJobFn(apply_dipcall_vcf_filter, alignments[0]))
workflow.exportFile(dipcall_filtered, makeURL(options.outputFile))
workflow.exportFile(
alignments[1],
makeURL(options.outputFile + ".unfiltered.secondary"))
else:
workflow.exportFile(alignments[0], makeURL(options.outputFile))
workflow.exportFile(alignments[1],
makeURL(options.outputFile + ".secondary"))
def runCactusGraphMap(options):
with Toil(options) as toil:
importSingularityImage(options)
#Run the workflow
if options.restart:
alignmentID = toil.restart()
else:
options.cactusDir = getTempDirectory()
# apply path overrides. this was necessary for wdl which doesn't take kindly to
# text files of local paths (ie seqfile). one way to fix would be to add support
# for s3 paths and force wdl to use it. a better way would be a more fundamental
# interface shift away from files of paths throughout all of cactus
if options.pathOverrides:
seqFile = SeqFile(options.seqFile)
configNode = ET.parse(options.configFile).getroot()
config = ConfigWrapper(configNode)
tree = MultiCactusTree(seqFile.tree)
tree.nameUnlabeledInternalNodes(
prefix=config.getDefaultInternalNodePrefix())
for name, override in zip(options.pathOverrideNames,
options.pathOverrides):
seqFile.pathMap[name] = override
override_seq = os.path.join(options.cactusDir,
'seqFile.override')
with open(override_seq, 'w') as out_sf:
out_sf.write(str(seqFile))
options.seqFile = override_seq
#load cactus config
configNode = ET.parse(options.configFile).getroot()
config = ConfigWrapper(configNode)
config.substituteAllPredefinedConstantsWithLiterals()
# get the minigraph "virutal" assembly name
graph_event = getOptionalAttrib(findRequiredNode(
configNode, "refgraph"),
"assemblyName",
default="__MINIGRAPH_SEQUENCES__")
# load the seqfile
seqFile = SeqFile(options.seqFile)
logger.info("Genomes for graphmap, {}".format(seqFile.pathMap))
if not options.outputFasta and graph_event not in seqFile.pathMap:
raise RuntimeError(
"{} assembly not found in seqfile so it must be specified with --outputFasta"
.format(graph_event))
#import the graph
gfa_id = toil.importFile(makeURL(options.minigraphGFA))
#import the sequences (that we need to align for the given event, ie leaves and outgroups)
seqIDMap = {}
for genome, seq in seqFile.pathMap.items():
if genome != graph_event:
if os.path.isdir(seq):
tmpSeq = getTempFile()
catFiles([
os.path.join(seq, subSeq)
for subSeq in os.listdir(seq)
], tmpSeq)
seq = tmpSeq
seq = makeURL(seq)
seqIDMap[genome] = toil.importFile(seq)
# run the workflow
paf_id, gfa_fa_id = toil.start(
Job.wrapJobFn(minigraph_workflow, options, config, seqIDMap,
gfa_id, graph_event))
#export the paf
toil.exportFile(paf_id, makeURL(options.outputPAF))
if gfa_fa_id:
toil.exportFile(gfa_fa_id, makeURL(options.outputFasta))
# update the input seqfile (in place!)
add_genome_to_seqfile(options.seqFile, makeURL(options.outputFasta),
graph_event)
class TestCase(unittest.TestCase):
def setUp(self):
unittest.TestCase.setUp(self)
self.trees = randomTreeSet()
self.mcTrees = []
self.tempDir = getTempDirectory(os.getcwd())
self.tempFa = os.path.join(self.tempDir, "seq.fa")
with open(self.tempFa, "w") as f:
f.write(">temp\nNNNNNNNCNNNNAAAAAAAAAAAAAAANNNNNNN\n")
self.dummySeqMaps = []
for tree in self.trees:
if tree.size() < 50:
mcTree = MultiCactusTree(tree, tree.degree())
seqMap = dict()
for i in mcTree.breadthFirstTraversal():
mcTree.setName(i, "Node%s" % str(i))
seqMap["Node%s" % str(i)] = self.tempFa
mcTree.computeSubtreeRoots()
mcTree.nameUnlabeledInternalNodes()
self.mcTrees.append(mcTree)
self.dummySeqMaps.append(seqMap)
# Boreoeutherian tree
borTree = '((((HUMAN:0.006969,CHIMP:0.009727)Anc7:0.025291,BABOON:0.044568)Anc6:0.11,(MOUSE:0.072818,RAT:0.081244)Anc5:0.260342)Anc4:0.023260,((DOG:0.07,CAT:0.07)Anc3:0.087381,(PIG:0.06,COW:0.06)Anc2:0.104728)Anc1:0.04)Anc0;'
self.borMcTree = MultiCactusTree(NXNewick().parseString(borTree, addImpliedRoots=False))
self.borMcTree.computeSubtreeRoots()
self.borMcTree.nameUnlabeledInternalNodes()
self.mcTrees.append(self.borMcTree)
# Eutherian backbone tree
backbone = '(((((((((((Homo_sapiens:0.00655,Pan_troglodytes:0.00684):0.00422,Gorilla_gorilla_gorilla:0.008964):0.009693,Pongo_abelii:0.01894):0.015511,Macaca_mulatta:0.043601):0.08444,Aotus_nancymaae:0.08):0.08,Microcebus_murinus:0.10612):0.043494,Galeopterus_variegatus:0.134937):0.04,((((Jaculus_jaculus:0.1,(Microtus_ochrogaster:0.14,(Mus_musculus:0.084509,Rattus_norvegicus:0.091589):0.047773):0.06015):0.122992,(Heterocephalus_glaber:0.1,(Cavia_porcellus:0.065629,(Chinchilla_lanigera:0.06,Octodon_degus:0.1):0.06):0.05):0.06015):0.05,Marmota_marmota:0.1):0.05,Oryctolagus_cuniculus:0.21569):0.04):0.040593,(((Sus_scrofa:0.12,(Orcinus_orca:0.069688,(Bos_taurus:0.04,Capra_hircus:0.04):0.09):0.045488):0.02,((Equus_caballus:0.109397,(Felis_catus:0.098612,(Canis_lupus_familiaris:0.052458,Mustela_putorius_furo:0.08):0.02):0.049845):0.02,(Pteropus_alecto:0.1,Eptesicus_fuscus:0.08):0.033706):0.03):0.025,Erinaceus_europaeus:0.278178):0.021227):0.023664,(((Loxodonta_africana:0.022242,Procavia_capensis:0.145358):0.076687,Chrysochloris_asiatica:0.04):0.05,Dasypus_novemcinctus:0.169809):0.02)backbone_root:0.234728,(Monodelphis_domestica:0.125686,Sarcophilus_harrisii:0.12):0.2151);'
self.backboneTree = MultiCactusTree(NXNewick().parseString(backbone, addImpliedRoots=False))
self.backboneTree.computeSubtreeRoots()
self.backboneTree.nameUnlabeledInternalNodes()
self.mcTrees.append(self.backboneTree)
seqLens = dict()
seqLens["HUMAN"] = 57553
seqLens["CHIMP"] = 57344
seqLens["BABOON"] = 58960
seqLens["MOUSE"] = 32750
seqLens["RAT"] = 38436
seqLens["DOG"] = 54187
seqLens["CAT"] = 50283
seqLens["PIG"] = 54843
seqLens["COW"] = 55508
self.blanchetteSeqMap = dict()
for event, seqLen in seqLens.items():
p = os.path.join(self.tempDir, event +".fa")
with open(p, "w") as f:
f.write(">%s\n" % event)
f.write(''.join(['A'] * seqLen))
f.write('\n')
self.blanchetteSeqMap[event] = p
def tearDown(self):
unittest.TestCase.tearDown(self)
system("rm -rf %s" % self.tempDir)
def testJustLeaves(self):
og = GreedyOutgroup()
og.importTree(self.borMcTree)
candidates = set([self.borMcTree.getName(x) for x in self.borMcTree.getLeaves()])
og.greedy(candidateSet=candidates, candidateChildFrac=2.)
assert og.ogMap['Anc1'][0][0] == 'HUMAN'
assert og.ogMap['Anc2'][0][0] in ['CAT', 'DOG']
assert og.ogMap['Anc3'][0][0] in ['PIG', 'COW']
assert og.ogMap['Anc4'][0][0] in ['CAT', 'DOG']
assert og.ogMap['Anc5'][0][0] == 'HUMAN'
assert og.ogMap['Anc6'][0][0] in ['CAT', 'DOG']
assert og.ogMap['Anc7'][0][0] == 'BABOON'
def testHeightTable(self):
"""Make sure the height-table is calculated correctly."""
og = GreedyOutgroup()
og.importTree(self.borMcTree)
htable = og.heightTable()
self.assertEquals(htable[self.borMcTree.getNodeId('HUMAN')], 0)
self.assertEquals(htable[self.borMcTree.getNodeId('PIG')], 0)
self.assertEquals(htable[self.borMcTree.getNodeId('RAT')], 0)
self.assertEquals(htable[self.borMcTree.getNodeId('Anc7')], 1)
self.assertEquals(htable[self.borMcTree.getNodeId('Anc1')], 2)
self.assertEquals(htable[self.borMcTree.getNodeId('Anc0')], 4)
def testZeroThreshold(self):
"""A threshold of 0 should produce outgroup sets that cause no additional depth in the resulting schedule."""
tree = self.backboneTree
og = GreedyOutgroup()
og.importTree(tree)
og.greedy(candidateSet=set(['Homo_sapiens', 'Mus_musculus']),threshold=0, maxNumOutgroups=3, candidateChildFrac=0.75)
og.greedy(threshold=0, maxNumOutgroups=3, candidateChildFrac=0.75)
htable = og.heightTable()
for node, outgroups in og.ogMap.items():
for outgroup, _ in outgroups:
# For the outgroup assignment to create no
# additional dependencies, each outgroup must have
# a height lower than the node it's outgroup to
# (or be a leaf)
self.assertTrue(htable[tree.getNodeId(outgroup)] < htable[tree.getNodeId(node)] \
or htable[tree.getNodeId(outgroup)] == 0)
def testCandidates(self):
og = GreedyOutgroup()
og.importTree(self.borMcTree)
candidates = set(['HUMAN', 'CHIMP', 'RAT'])
og.greedy(candidateSet=candidates, candidateChildFrac=0.5)
assert og.ogMap['Anc1'][0][0] == 'Anc4'
assert og.ogMap['Anc2'][0][0] == 'Anc4'
assert og.ogMap['Anc3'][0][0] == 'Anc4'
assert 'Anc4' not in og.ogMap
assert og.ogMap['Anc5'][0][0] in ['HUMAN', 'CHIMP', 'Anc6', 'Anc7']
assert og.ogMap['Anc6'][0][0] in ['Anc5', 'MOUSE', 'RAT']
assert og.ogMap['Anc7'][0][0] in ['Anc5', 'MOUSE', 'RAT']
og = GreedyOutgroup()
og.importTree(self.borMcTree)
candidates = set(['HUMAN', 'CHIMP', 'RAT'])
og.greedy(candidateSet=candidates, candidateChildFrac=1.0)
assert og.ogMap['Anc1'][0][0] == 'Anc7'
assert og.ogMap['Anc2'][0][0] == 'Anc7'
assert og.ogMap['Anc3'][0][0] == 'Anc7'
assert 'Anc4' not in og.ogMap
assert og.ogMap['Anc5'][0][0] in ['HUMAN', 'CHIMP', 'Anc7']
assert og.ogMap['Anc6'][0][0] == 'RAT'
assert og.ogMap['Anc7'][0][0] == 'RAT'
def testGeneralBetterThanLeaves(self):
for tree in self.mcTrees:
og1 = GreedyOutgroup()
og1.importTree(tree)
candidates = set([tree.getName(x) for x in tree.getLeaves()])
og1.greedy(candidateSet=candidates, candidateChildFrac=2.)
og2 = GreedyOutgroup()
og2.importTree(tree)
og2.greedy(candidateSet=None)
for i in og1.ogMap:
assert i in og2.ogMap
dist1 = og1.ogMap[i][0][1]
dist2 = og2.ogMap[i][0][1]
assert dist2 <= dist1
def testGeneralConstrainedBetterThanLeaves(self):
for tree in self.mcTrees:
og1 = GreedyOutgroup()
og1.importTree(tree)
candidates = set([tree.getName(x) for x in tree.getLeaves()])
og1.greedy(candidateSet=candidates, candidateChildFrac=2.)
og2 = GreedyOutgroup()
og2.importTree(tree)
og2.greedy(candidateSet=None, threshold=2)
for i in og1.ogMap:
assert i in og2.ogMap
dist1 = og1.ogMap[i][0][1]
dist2 = og2.ogMap[i][0][1]
assert dist2 <= dist1
def testMultipleOutgroups(self):
og = GreedyOutgroup()
og.importTree(self.borMcTree)
og.greedy(candidateChildFrac=0.5, maxNumOutgroups=3)
# make sure all entries have <= 3 outgroups.
assert all(map(lambda x: len(x) <= 3, og.ogMap.values()))
# and for all entries, the closest must be first.
assert all(map(lambda x: x == sorted(x, key=itemgetter(1)),
og.ogMap.values()))
# ordering is important!
assert map(itemgetter(0), og.ogMap['Anc4']) == ['Anc1']
assert map(itemgetter(0), og.ogMap['Anc7']) == ['BABOON', 'Anc1',
'Anc5']
# We avoid cycles, and choose post-order first, so this only
# uses leaves.
assert map(itemgetter(0), og.ogMap['Anc1']) == ['HUMAN', 'CHIMP',
'BABOON']
def testMultipleOutgroupsJustLeaves(self):
og = GreedyOutgroup()
og.importTree(self.borMcTree)
candidates = set([self.borMcTree.getName(x) for x in self.borMcTree.getLeaves()])
og.greedy(candidateSet=candidates, candidateChildFrac=2.,
maxNumOutgroups=3)
# make sure all entries have <= 3 outgroups.
assert all(map(lambda x: len(x) <= 3, og.ogMap.values()))
# and for all entries, the closest must be first.
assert all(map(lambda x: x == sorted(x, key=itemgetter(1)),
og.ogMap.values()))
# ordering is important!
assert map(itemgetter(0), og.ogMap['Anc1']) == ['HUMAN', 'CHIMP',
'BABOON']
assert og.ogMap['Anc7'][0][0] == 'BABOON'
assert og.ogMap['Anc7'][1][0] in ['CAT', 'DOG']
assert og.ogMap['Anc7'][2][0] in ['CAT', 'DOG']
def testMultipleOutgroupsOnRandomTrees(self):
for tree in self.mcTrees:
og = GreedyOutgroup()
og.importTree(tree)
og.greedy(candidateChildFrac=0.5, maxNumOutgroups=3)
# make sure all entries have <= 3 outgroups.
assert all(map(lambda x: len(x) <= 3, og.ogMap.values()))
# and for all entries, the closest must be first.
assert all(map(lambda x: x == sorted(x, key=itemgetter(1)),
og.ogMap.values()))
def testDynamicOutgroupsOnRandomTrees(self):
for tree, seqMap in zip(self.mcTrees, self.dummySeqMaps):
degree = max([len(tree.getChildren(x)) for x in
tree.breadthFirstTraversal()])
if degree < 8:
og = DynamicOutgroup()
og.edgeLen = 5
og.importTree(tree, seqMap)
og.compute(maxNumOutgroups=3)
# make sure all entries have <= 3 outgroups.
assert all(map(lambda x: len(x) <= 3, og.ogMap.values()))
# and for all entries, the closest must be first.
# (this will be true because all sequences are the same)
assert all(map(lambda x: x == sorted(x, key=itemgetter(1)),
og.ogMap.values()))
def testDynamicOutgroupsJustLeaves(self):
og = DynamicOutgroup()
og.importTree(self.borMcTree, self.blanchetteSeqMap)
og.compute(maxNumOutgroups=3, sequenceLossWeight=0.)
# make sure all entries have <= 3 outgroups.
assert all(map(lambda x: len(x) <= 3, og.ogMap.values()))
# and for all entries, the closest must be first.
assert all(map(lambda x: x == sorted(x, key=itemgetter(1)),
og.ogMap.values()))
# ordering is important!
assert og.ogMap['Anc1'][0][0] == 'HUMAN'
assert og.ogMap['Anc7'][0][0] == 'BABOON'
og = DynamicOutgroup()
og.importTree(self.borMcTree, self.blanchetteSeqMap)
og.compute(maxNumOutgroups=3)
# make sure all entries have <= 3 outgroups.
assert all(map(lambda x: len(x) <= 3, og.ogMap.values()))
# we keep dynamic outgroups sorted by distance too
assert all(map(lambda x: x == sorted(x, key=itemgetter(1)),
og.ogMap.values()))
def testMultipleIdenticalRunsProduceSameResult(self):
"""The code now allows for multiple greedy() calls with different
candidate sets, so that some outgroups can be 'preferred' over
others without being the only candidates.
Check that running greedy() multiple times with the same
parameters gives the same result as running it once.
"""
for tree in self.mcTrees:
ogOnce = GreedyOutgroup()
ogOnce.importTree(tree)
ogOnce.greedy(maxNumOutgroups=3)
ogMultipleTimes = GreedyOutgroup()
ogMultipleTimes.importTree(tree)
ogMultipleTimes.greedy(maxNumOutgroups=3)
ogMultipleTimes.greedy(maxNumOutgroups=3)
ogMultipleTimes.greedy(maxNumOutgroups=3)
# make sure all entries have <= 3 outgroups.
assert all(map(lambda x: len(x) <= 3, ogMultipleTimes.ogMap.values()))
# and for all entries, the closest must be first.
assert all(map(lambda x: x == sorted(x, key=itemgetter(1)),
ogMultipleTimes.ogMap.values()))
# Check that the maps are equal. Can't compare them
# directly since python will convert them to ordered
# association lists.
assert len(ogOnce.ogMap) == len(ogMultipleTimes.ogMap)
for i in ogOnce.ogMap:
assert i in ogMultipleTimes.ogMap
assert ogOnce.ogMap[i] == ogMultipleTimes.ogMap[i]
def testPreferredCandidateSets(self):
"""Test that running greedy() multiple times with different candidate
sets will behave properly, i.e. keep all the existing outgroup
assignments and fill in more on the second run."""
for tree in self.mcTrees:
ogOnce = GreedyOutgroup()
ogOnce.importTree(tree)
nodes = [j for j in tree.postOrderTraversal()]
candidateSet = set([tree.getName(i) for i in random.sample(nodes, min(20, len(nodes)))])
ogOnce.greedy(candidateSet=candidateSet, maxNumOutgroups=3)
ogTwice = GreedyOutgroup()
ogTwice.importTree(tree)
ogTwice.greedy(candidateSet=candidateSet, maxNumOutgroups=3)
ogTwice.greedy(maxNumOutgroups=3)
# make sure all entries have <= 3 outgroups.
assert all(map(lambda x: len(x) <= 3, ogTwice.ogMap.values()))
# and for all entries, the closest must be first.
assert all(map(lambda x: x == sorted(x, key=itemgetter(1)),
ogTwice.ogMap.values()))
for node in ogTwice.ogMap:
if node in ogOnce.ogMap:
# the ogMap entry in ogOnce should be a subset of the ogMap entry for ogTwice
oneRunOutgroups = ogOnce.ogMap[node]
twoRunOutgroups = ogTwice.ogMap[node]
assert len(twoRunOutgroups) >= len(oneRunOutgroups)
for i in oneRunOutgroups:
assert i in twoRunOutgroups
def testNoOutgroupIsADescendantOfAnother(self):
"""No two outgroups should be on the same path to the root."""
for tree in self.mcTrees:
tree.nameUnlabeledInternalNodes()
og = GreedyOutgroup()
og.importTree(tree)
og.greedy(maxNumOutgroups=3)
for source in og.ogMap:
for (sink1, _) in og.ogMap[source]:
for (sink2, _) in og.ogMap[source]:
if sink1 != sink2:
sink1Id = tree.nameToId[sink1]
sink2Id = tree.nameToId[sink2]
assert sink1Id not in tree.postOrderTraversal(sink2Id)
assert sink2Id not in tree.postOrderTraversal(sink1Id)
class TestCase(unittest.TestCase):
def setUp(self):
unittest.TestCase.setUp(self)
self.mcTree1 = None
self.mcTree1a = None
self.mcTree2 = None
self.__generateTrees()
def testSanity(self):
parser = NXNewick()
mcTree1 = MultiCactusTree(
parser.parseString(self.tree1, addImpliedRoots=False))
tree1String = NXNewick().writeString(mcTree1)
self.assertEqual(tree1String, self.tree1)
mcTree2 = MultiCactusTree(parser.parseString(self.tree2,
addImpliedRoots=False),
subtreeSize=3)
tree2String = NXNewick().writeString(mcTree2)
self.assertEqual(tree2String, self.tree2)
def testSubtrees(self):
roots1 = [
"Anc0", "Anc1", "Anc2", "Anc3", "Anc4", "Anc5", "Anc6", "Anc7"
]
roots1a = ["Anc0", "Anc3", "Anc4", "Anc5", "Anc6"]
roots2 = ["Anc0", "Anc1", "Anc2", "Anc3", "Anc4"]
subTree1_a3 = '(Anc7:0.025291,BABOON:0.044568)Anc3;'
subTree1a_a0 = '((Anc3:0.11,Anc4:0.260342)Anc1:0.02326,(Anc5:0.087381,Anc6:0.104728)Anc2:0.04)Anc0;'
subTree2_a3 = '(monkey:100.8593,cat:47.14069)Anc5;'
trueRoots = [roots1, roots1a, roots2]
trueSubtrees = [subTree1_a3, subTree1a_a0, subTree2_a3]
trees = [self.mcTree1, self.mcTree1a, self.mcTree2]
ancs = ["Anc3", "Anc0", "Anc5"]
for i in range(0, 3):
roots = trees[i].getSubtreeRootNames()
self.assertEqual(sorted(roots), sorted(trueRoots[i]))
subtree = trees[i].extractSubTree(ancs[i])
subtree = NXNewick().writeString(subtree)
self.assertEqual(subtree, trueSubtrees[i])
def testAddSelf(self):
trueSelf = '((((((((HUMAN:0.006969)HUMAN_self:0.006969,(CHIMP:0.009727)CHIMP_self:0.009727)Anc7:0.025291)Anc7_self:0.025291,(BABOON:0.044568)BABOON_self:0.044568)Anc3:0.11)Anc3_self:0.11,(((MOUSE:0.072818)MOUSE_self:0.072818,(RAT:0.081244)RAT_self:0.081244)Anc4:0.260342)Anc4_self:0.260342)Anc1:0.02326)Anc1_self:0.02326,(((((DOG:0.07)DOG_self:0.07,(CAT:0.07)CAT_self:0.07)Anc5:0.087381)Anc5_self:0.087381,(((PIG:0.06)PIG_self:0.06,(COW:0.06)COW_self:0.06)Anc6:0.104728)Anc6_self:0.104728)Anc2:0.04)Anc2_self:0.04)Anc0;'
tree = MultiCactusTree(self.mcTree1)
tree.nameUnlabeledInternalNodes()
tree.computeSubtreeRoots()
tree.addSelfEdges()
treeString = NXNewick().writeString(tree)
self.assertEqual(treeString, trueSelf)
def testAddOutgroup(self):
trueOg = '((((HUMAN:0.006969,CHIMP:0.009727)Anc7:0.025291,BABOON:0.044568)Anc3:0.11,(MOUSE:0.072818,RAT:0.081244)Anc4:0.260342)Anc1:0.02326,((DOG:0.07,CAT:0.07)Anc5:0.087381,(PIG:0.06,COW:0.06)Anc6:0.104728)Anc2:0.04,outgroup:1.7)Anc0;'
tree = MultiCactusTree(self.mcTree1)
tree.nameUnlabeledInternalNodes()
tree.computeSubtreeRoots()
tree.addOutgroup("outgroup", 1.7)
treeString = NXNewick().writeString(tree)
self.assertEqual(treeString, trueOg)
trueLeafOg = "(A:1.1,outgroup:1.1);"
leafTreeString = "A;"
parser = NXNewick()
leafTree = MultiCactusTree(
parser.parseString(leafTreeString, addImpliedRoots=False))
leafTree.nameUnlabeledInternalNodes()
leafTree.computeSubtreeRoots()
leafTree.addOutgroup("outgroup", 2.2)
leafTreeOutString = NXNewick().writeString(leafTree)
self.assertEqual(leafTreeOutString, trueLeafOg)
def testExtractSpanningTree(self):
"""Tests whether extracting a binary spanning tree works correctly."""
prevNewick1 = NXNewick().writeString(self.mcTree1)
# Check a dead-simple spanning tree with 3 closely related leaves.
spanHCB = self.mcTree1.extractSpanningTree(
["HUMAN", "CHIMP", "BABOON"])
# Check that the existing tree hasn't been modified (OK, a bit
# silly, but just in case).
self.assertEqual(NXNewick().writeString(self.mcTree1), prevNewick1)
# Check the actual spanning tree.
self.assertEqual(
NXNewick().writeString(spanHCB),
"((HUMAN:0.006969,CHIMP:0.009727)Anc7:0.025291,BABOON:0.044568)Anc3;"
)
# Now test a more complicated tree, where we should remove as
# many of the ancestors as possible (they will add extra
# losses for no reason!).
spanHCC = self.mcTree1.extractSpanningTree(["HUMAN", "CHIMP", "CAT"])
self.assertEqual(NXNewick().writeString(self.mcTree1), prevNewick1)
self.assertEqual(
NXNewick().writeString(spanHCC),
"((HUMAN:0.006969,CHIMP:0.009727)Anc7:0.158551,CAT:0.197381)Anc0;")
def __generateTrees(self):
self.tree1 = '((((HUMAN:0.006969,CHIMP:0.009727):0.025291,BABOON:0.044568):0.11,(MOUSE:0.072818,RAT:0.081244):0.260342):0.02326,((DOG:0.07,CAT:0.07):0.087381,(PIG:0.06,COW:0.06):0.104728):0.04);'
self.tree2 = '((raccoon:19.19959,bear:6.80041):0.846,((sea_lion:11.997,seal:12.003):7.52973,((monkey:100.8593,cat:47.14069):20.59201,weasel:18.87953):2.0946):3.87382,dog:25.46154);'
parser = NXNewick()
self.mcTree1 = MultiCactusTree(
parser.parseString(self.tree1, addImpliedRoots=False))
self.mcTree1a = MultiCactusTree(parser.parseString(
self.tree1, addImpliedRoots=False),
subtreeSize=4)
self.mcTree2 = MultiCactusTree(parser.parseString(
self.tree2, addImpliedRoots=False),
subtreeSize=3)
self.mcTree1.nameUnlabeledInternalNodes()
self.mcTree1a.nameUnlabeledInternalNodes()
self.mcTree2.nameUnlabeledInternalNodes()
self.mcTree1.computeSubtreeRoots()
self.mcTree1a.computeSubtreeRoots()
self.mcTree2.computeSubtreeRoots()
def createMCProject(tree, experiment, config, options):
mcTree = MultiCactusTree(tree, config.getSubtreeSize())
mcTree.nameUnlabeledInternalNodes(config.getDefaultInternalNodePrefix())
mcTree.computeSubtreeRoots()
mcProj = MultiCactusProject()
mcProj.mcTree = mcTree
mcProj.inputSequences = experiment.getSequences()[:]
if config.getDoSelfAlignment():
mcTree.addSelfEdges()
for name in mcProj.mcTree.getSubtreeRootNames():
expPath = "%s/%s/%s_experiment.xml" % (options.path, name, name)
mcProj.expMap[name] = os.path.abspath(expPath)
alignmentRootId = mcProj.mcTree.getRootId()
if options.root is not None:
try:
alignmentRootId = mcProj.mcTree.getNodeId(options.root)
except:
raise RuntimeError("Specified root name %s not found in tree" %
options.root)
mcProj.outgroup = None
if config.getOutgroupStrategy() == 'greedy':
# use the provided outgroup candidates, or use all outgroups
# as candidates if none are given
mcProj.outgroup = GreedyOutgroup()
mcProj.outgroup.importTree(mcProj.mcTree, alignmentRootId)
mcProj.outgroup.greedy(
threshold=config.getOutgroupThreshold(),
candidateSet=options.outgroupNames,
candidateChildFrac=config.getOutgroupAncestorQualityFraction(),
maxNumOutgroups=config.getMaxNumOutgroups())
elif config.getOutgroupStrategy() == 'greedyLeaves':
# use all leaves as outgroups, unless outgroup candidates are given
mcProj.outgroup = GreedyOutgroup()
mcProj.outgroup.importTree(mcProj.mcTree, alignmentRootId)
ogSet = options.outgroupNames
if ogSet is None:
ogSet = set(
[mcProj.mcTree.getName(x) for x in mcProj.mcTree.getLeaves()])
mcProj.outgroup.greedy(threshold=config.getOutgroupThreshold(),
candidateSet=ogSet,
candidateChildFrac=2.0,
maxNumOutgroups=config.getMaxNumOutgroups())
elif config.getOutgroupStrategy() == 'greedyPreference':
# prefer the provided outgroup candidates, if any, but use
# other nodes as "filler" if we can't find enough.
mcProj.outgroup = GreedyOutgroup()
mcProj.outgroup.importTree(mcProj.mcTree, alignmentRootId)
mcProj.outgroup.greedy(
threshold=config.getOutgroupThreshold(),
candidateSet=options.outgroupNames,
candidateChildFrac=config.getOutgroupAncestorQualityFraction(),
maxNumOutgroups=config.getMaxNumOutgroups())
mcProj.outgroup.greedy(
threshold=config.getOutgroupThreshold(),
candidateSet=None,
candidateChildFrac=config.getOutgroupAncestorQualityFraction(),
maxNumOutgroups=config.getMaxNumOutgroups())
elif config.getOutgroupStrategy() == 'dynamic':
# dynamic programming algorithm that exactly optimizes probability
# that base in target node aligns to at least one base in the
# outgroup set. Caveats are that it only returns leaves, and
# the model used for optimization is super naive. Still, it does
# some things better than greedy approaches such as properly account
# for phylogenetic redundancy, as well as try to factor assembly
# size/quality automatically.
mcProj.outgroup = DynamicOutgroup()
mcProj.outgroup.importTree(mcProj.mcTree,
mcProj.getInputSequenceMap(),
alignmentRootId,
candidateSet=options.outgroupNames)
mcProj.outgroup.compute(maxNumOutgroups=config.getMaxNumOutgroups())
elif config.getOutgroupStrategy() != 'none':
raise RuntimeError("Could not understand outgroup strategy %s" %
config.getOutgroupStrategy())
# if necessary, we reroot the tree at the specified alignment root id. all leaf genomes
# that are no longer in the tree, but still used as outgroups, are moved into special fields
# so that we can remember to, say, get their paths for preprocessing.
specifyAlignmentRoot(mcProj, alignmentRootId)
return mcProj
def cactusPrepare(options, project):
""" annotate a SeqFile with ancestral names as well as paths for output sequences."""
# read the input
seqFile = SeqFile(options.seqFile)
configNode = ET.parse(options.configFile).getroot()
config = ConfigWrapper(configNode)
if not options.wdl:
# prepare output sequence directory
# todo: support remote (ie s3) output directory
try:
os.makedirs(options.outDir)
except:
pass
if not os.path.isdir(options.outDir):
raise RuntimeError('Unable to create output sequence directory \'{}\''.format(options.outDir))
if not os.access(options.outDir, os.W_OK):
logger.warning('Output sequence directory is not writeable: \'{}\''.format(options.outDir))
if options.preprocessOnly or options.gpu:
if options.preprocessOnly:
# hack the configfile to skip preprocessing and write it to the output dir
config.removePreprocessors()
if options.gpu:
# hack the configfile to toggle on gpu lastz
cafNode = findRequiredNode(config.xmlRoot, "caf")
cafNode.attrib["gpuLastz"] = "true"
# realigning doesn't mix well with lastz so we make sure it's off
# https://github.com/ComparativeGenomicsToolkit/cactus/issues/271
cafNode.attrib["realign"] = "0"
options.configFile = os.path.join(options.outDir, 'config-prepared.xml')
sys.stderr.write("configuration saved in {}\n".format(options.configFile))
config.writeXML(options.configFile)
# pass through the config file to the options
# todo (don't like second hard-code check of .xml path)
if options.configFile != os.path.join(cactusRootPath(), "cactus_progressive_config.xml") and not options.wdl:
options.cactusOptions += ' --configFile {}'.format(options.configFile)
# get the ancestor names
tree = MultiCactusTree(seqFile.tree)
tree.nameUnlabeledInternalNodes(prefix = config.getDefaultInternalNodePrefix())
# make the output
outSeqFile = SeqFile()
outSeqFile.tree= tree
outSeqFile.pathMap = copy.deepcopy(seqFile.pathMap)
outSeqFile.outgroups = copy.deepcopy(seqFile.outgroups)
# update paths for preprocessed leaves or inferred ancestors
for node in outSeqFile.tree.breadthFirstTraversal():
name = outSeqFile.tree.getName(node)
leaf = outSeqFile.tree.isLeaf(node)
if leaf or (not leaf and name not in seqFile.pathMap and not options.preprocessOnly):
out_basename = seqFile.pathMap[name] if name in seqFile.pathMap else '{}.fa'.format(name)
outSeqFile.pathMap[name] = os.path.join(options.outDir, os.path.basename(out_basename))
if options.wdl:
# uniquify name in wdl to prevent collisions
outSeqFile.pathMap[name] += '.pp'
# write the output
if options.outSeqFile:
with open(options.outSeqFile, 'w') as out_sf:
out_sf.write(str(outSeqFile))
# write the instructions
print(get_plan(options, project, seqFile, outSeqFile))
def runCactusBlastOnly(options):
with Toil(options) as toil:
importSingularityImage(options)
#Run the workflow
if options.restart:
alignmentID = toil.restart()
else:
options.cactusDir = getTempDirectory()
# apply path overrides. this was necessary for wdl which doesn't take kindly to
# text files of local paths (ie seqfile). one way to fix would be to add support
# for s3 paths and force wdl to use it. a better way would be a more fundamental
# interface shift away from files of paths throughout all of cactus
if options.pathOverrides:
seqFile = SeqFile(options.seqFile)
configNode = ET.parse(options.configFile).getroot()
config = ConfigWrapper(configNode)
tree = MultiCactusTree(seqFile.tree)
tree.nameUnlabeledInternalNodes(
prefix=config.getDefaultInternalNodePrefix())
for name, override in zip(options.pathOverrideNames,
options.pathOverrides):
seqFile.pathMap[name] = override
override_seq = os.path.join(options.cactusDir,
'seqFile.override')
with open(override_seq, 'w') as out_sf:
out_sf.write(str(seqFile))
options.seqFile = override_seq
#to be consistent with all-in-one cactus, we make sure the project
#isn't limiting itself to the subtree (todo: parameterize so root can
#be passed through from prepare to blast/align)
proj_options = copy.deepcopy(options)
proj_options.root = None
#Create the progressive cactus project (as we do in runCactusProgressive)
projWrapper = ProjectWrapper(proj_options,
proj_options.configFile,
ignoreSeqPaths=options.root)
projWrapper.writeXml()
pjPath = os.path.join(
options.cactusDir, ProjectWrapper.alignmentDirName,
'%s_project.xml' % ProjectWrapper.alignmentDirName)
assert os.path.exists(pjPath)
project = MultiCactusProject()
if not os.path.isdir(options.cactusDir):
os.makedirs(options.cactusDir)
project.readXML(pjPath)
# open up the experiment (as we do in ProgressiveUp.run)
# note that we copy the path into the options here
experimentFile = project.expMap[options.root]
expXml = ET.parse(experimentFile).getroot()
logger.info("Experiment {}".format(ET.tostring(expXml)))
experiment = ExperimentWrapper(expXml)
configPath = experiment.getConfigPath()
configXml = ET.parse(configPath).getroot()
seqIDMap = dict()
tree = MultiCactusTree(experiment.getTree()).extractSubTree(
options.root)
leaves = tree.getChildNames(tree.getRootName())
outgroups = experiment.getOutgroupGenomes()
genome_set = set(leaves + outgroups)
logger.info("Genomes in blastonly, {}: {}".format(
options.root, list(genome_set)))
print(str(project.inputSequenceMap))
#import the sequences (that we need to align for the given event, ie leaves and outgroups)
for genome, seq in list(project.inputSequenceMap.items()):
if genome in genome_set:
if os.path.isdir(seq):
tmpSeq = getTempFile()
catFiles([
os.path.join(seq, subSeq)
for subSeq in os.listdir(seq)
], tmpSeq)
seq = tmpSeq
seq = makeURL(seq)
project.inputSequenceIDMap[genome] = toil.importFile(seq)
else:
# out-of-scope sequences will only cause trouble later on
del project.inputSequenceMap[genome]
#import cactus config
if options.configFile:
cactusConfigID = toil.importFile(makeURL(options.configFile))
else:
cactusConfigID = toil.importFile(
makeURL(project.getConfigPath()))
project.setConfigID(cactusConfigID)
project.syncToFileStore(toil)
configNode = ET.parse(project.getConfigPath()).getroot()
configWrapper = ConfigWrapper(configNode)
configWrapper.substituteAllPredefinedConstantsWithLiterals()
workFlowArgs = CactusWorkflowArguments(
options,
experimentFile=experimentFile,
configNode=configNode,
seqIDMap=project.inputSequenceIDMap)
outWorkFlowArgs = toil.start(
CactusTrimmingBlastPhase(standAlone=True,
cactusWorkflowArguments=workFlowArgs,
phaseName="trimBlast"))
# export the alignments
toil.exportFile(outWorkFlowArgs.alignmentsID,
makeURL(options.outputFile))
# optional secondary alignments
if outWorkFlowArgs.secondaryAlignmentsID:
toil.exportFile(outWorkFlowArgs.secondaryAlignmentsID,
makeURL(options.outputFile) + '.secondary')
# outgroup fragments and coverage are necessary for cactus-align, as the sequence names got changed in the above alignemnts
for i, outgroupFragmentID in enumerate(
outWorkFlowArgs.outgroupFragmentIDs):
toil.exportFile(
outgroupFragmentID,
makeURL(options.outputFile) + '.og_fragment_{}'.format(i))
# cactus-align can recompute coverage on the fly, but we save them because we have them
for i, ingroupCoverageID in enumerate(
outWorkFlowArgs.ingroupCoverageIDs):
toil.exportFile(
ingroupCoverageID,
makeURL(options.outputFile) + '.ig_coverage_{}'.format(i))
class TestCase(unittest.TestCase):
def setUp(self):
unittest.TestCase.setUp(self)
self.trees = randomTreeSet()
self.mcTrees = []
self.tempDir = getTempDirectory(os.getcwd())
self.tempFa = os.path.join(self.tempDir, "seq.fa")
with open(self.tempFa, "w") as f:
f.write(">temp\nNNNNNNNCNNNNAAAAAAAAAAAAAAANNNNNNN\n")
self.dummySeqMaps = []
for tree in self.trees:
if tree.size() < 50:
mcTree = MultiCactusTree(tree)
seqMap = dict()
for i in mcTree.breadthFirstTraversal():
mcTree.setName(i, "Node%s" % str(i))
seqMap["Node%s" % str(i)] = self.tempFa
mcTree.computeSubtreeRoots()
mcTree.nameUnlabeledInternalNodes()
self.mcTrees.append(mcTree)
self.dummySeqMaps.append(seqMap)
# Boreoeutherian tree
borTree = '((((HUMAN:0.006969,CHIMP:0.009727)Anc7:0.025291,BABOON:0.044568)Anc6:0.11,(MOUSE:0.072818,RAT:0.081244)Anc5:0.260342)Anc4:0.023260,((DOG:0.07,CAT:0.07)Anc3:0.087381,(PIG:0.06,COW:0.06)Anc2:0.104728)Anc1:0.04)Anc0;'
self.borMcTree = MultiCactusTree(NXNewick().parseString(
borTree, addImpliedRoots=False))
self.borMcTree.computeSubtreeRoots()
self.borMcTree.nameUnlabeledInternalNodes()
self.mcTrees.append(self.borMcTree)
# Eutherian backbone tree
backbone = '(((((((((((Homo_sapiens:0.00655,Pan_troglodytes:0.00684):0.00422,Gorilla_gorilla_gorilla:0.008964):0.009693,Pongo_abelii:0.01894):0.015511,Macaca_mulatta:0.043601):0.08444,Aotus_nancymaae:0.08):0.08,Microcebus_murinus:0.10612):0.043494,Galeopterus_variegatus:0.134937):0.04,((((Jaculus_jaculus:0.1,(Microtus_ochrogaster:0.14,(Mus_musculus:0.084509,Rattus_norvegicus:0.091589):0.047773):0.06015):0.122992,(Heterocephalus_glaber:0.1,(Cavia_porcellus:0.065629,(Chinchilla_lanigera:0.06,Octodon_degus:0.1):0.06):0.05):0.06015):0.05,Marmota_marmota:0.1):0.05,Oryctolagus_cuniculus:0.21569):0.04):0.040593,(((Sus_scrofa:0.12,(Orcinus_orca:0.069688,(Bos_taurus:0.04,Capra_hircus:0.04):0.09):0.045488):0.02,((Equus_caballus:0.109397,(Felis_catus:0.098612,(Canis_lupus_familiaris:0.052458,Mustela_putorius_furo:0.08):0.02):0.049845):0.02,(Pteropus_alecto:0.1,Eptesicus_fuscus:0.08):0.033706):0.03):0.025,Erinaceus_europaeus:0.278178):0.021227):0.023664,(((Loxodonta_africana:0.022242,Procavia_capensis:0.145358):0.076687,Chrysochloris_asiatica:0.04):0.05,Dasypus_novemcinctus:0.169809):0.02)backbone_root:0.234728,(Monodelphis_domestica:0.125686,Sarcophilus_harrisii:0.12):0.2151);'
self.backboneTree = MultiCactusTree(NXNewick().parseString(
backbone, addImpliedRoots=False))
self.backboneTree.computeSubtreeRoots()
self.backboneTree.nameUnlabeledInternalNodes()
self.mcTrees.append(self.backboneTree)
seqLens = dict()
seqLens["HUMAN"] = 57553
seqLens["CHIMP"] = 57344
seqLens["BABOON"] = 58960
seqLens["MOUSE"] = 32750
seqLens["RAT"] = 38436
seqLens["DOG"] = 54187
seqLens["CAT"] = 50283
seqLens["PIG"] = 54843
seqLens["COW"] = 55508
self.blanchetteSeqMap = dict()
for event, seqLen in seqLens.items():
p = os.path.join(self.tempDir, event + ".fa")
with open(p, "w") as f:
f.write(">%s\n" % event)
f.write(''.join(['A'] * seqLen))
f.write('\n')
self.blanchetteSeqMap[event] = p
def tearDown(self):
unittest.TestCase.tearDown(self)
system("rm -rf %s" % self.tempDir)
def testJustLeaves(self):
og = GreedyOutgroup()
og.importTree(self.borMcTree)
candidates = set(
[self.borMcTree.getName(x) for x in self.borMcTree.getLeaves()])
og.greedy(candidateSet=candidates, candidateChildFrac=2.)
assert og.ogMap['Anc1'][0][0] == 'HUMAN'
assert og.ogMap['Anc2'][0][0] in ['CAT', 'DOG']
assert og.ogMap['Anc3'][0][0] in ['PIG', 'COW']
assert og.ogMap['Anc4'][0][0] in ['CAT', 'DOG']
assert og.ogMap['Anc5'][0][0] == 'HUMAN'
assert og.ogMap['Anc6'][0][0] in ['CAT', 'DOG']
assert og.ogMap['Anc7'][0][0] == 'BABOON'
def testHeightTable(self):
"""Make sure the height-table is calculated correctly."""
og = GreedyOutgroup()
og.importTree(self.borMcTree)
htable = og.heightTable()
self.assertEquals(htable[self.borMcTree.getNodeId('HUMAN')], 0)
self.assertEquals(htable[self.borMcTree.getNodeId('PIG')], 0)
self.assertEquals(htable[self.borMcTree.getNodeId('RAT')], 0)
self.assertEquals(htable[self.borMcTree.getNodeId('Anc7')], 1)
self.assertEquals(htable[self.borMcTree.getNodeId('Anc1')], 2)
self.assertEquals(htable[self.borMcTree.getNodeId('Anc0')], 4)
def testZeroThreshold(self):
"""A threshold of 0 should produce outgroup sets that cause no additional depth in the resulting schedule."""
tree = self.backboneTree
og = GreedyOutgroup()
og.importTree(tree)
og.greedy(candidateSet=set(['Homo_sapiens', 'Mus_musculus']),
threshold=0,
maxNumOutgroups=3,
candidateChildFrac=0.75)
og.greedy(threshold=0, maxNumOutgroups=3, candidateChildFrac=0.75)
htable = og.heightTable()
for node, outgroups in og.ogMap.items():
for outgroup, _ in outgroups:
# For the outgroup assignment to create no
# additional dependencies, each outgroup must have
# a height lower than the node it's outgroup to
# (or be a leaf)
self.assertTrue(htable[tree.getNodeId(outgroup)] < htable[tree.getNodeId(node)] \
or htable[tree.getNodeId(outgroup)] == 0)
def testCandidates(self):
og = GreedyOutgroup()
og.importTree(self.borMcTree)
candidates = set(['HUMAN', 'CHIMP', 'RAT'])
og.greedy(candidateSet=candidates, candidateChildFrac=0.5)
assert og.ogMap['Anc1'][0][0] == 'Anc4'
assert og.ogMap['Anc2'][0][0] == 'Anc4'
assert og.ogMap['Anc3'][0][0] == 'Anc4'
assert 'Anc4' not in og.ogMap
assert og.ogMap['Anc5'][0][0] in ['HUMAN', 'CHIMP', 'Anc6', 'Anc7']
assert og.ogMap['Anc6'][0][0] in ['Anc5', 'MOUSE', 'RAT']
assert og.ogMap['Anc7'][0][0] in ['Anc5', 'MOUSE', 'RAT']
og = GreedyOutgroup()
og.importTree(self.borMcTree)
candidates = set(['HUMAN', 'CHIMP', 'RAT'])
og.greedy(candidateSet=candidates, candidateChildFrac=1.0)
assert og.ogMap['Anc1'][0][0] == 'Anc7'
assert og.ogMap['Anc2'][0][0] == 'Anc7'
assert og.ogMap['Anc3'][0][0] == 'Anc7'
assert 'Anc4' not in og.ogMap
assert og.ogMap['Anc5'][0][0] in ['HUMAN', 'CHIMP', 'Anc7']
assert og.ogMap['Anc6'][0][0] == 'RAT'
assert og.ogMap['Anc7'][0][0] == 'RAT'
def testGeneralBetterThanLeaves(self):
for tree in self.mcTrees:
og1 = GreedyOutgroup()
og1.importTree(tree)
candidates = set([tree.getName(x) for x in tree.getLeaves()])
og1.greedy(candidateSet=candidates, candidateChildFrac=2.)
og2 = GreedyOutgroup()
og2.importTree(tree)
og2.greedy(candidateSet=None)
for i in og1.ogMap:
assert i in og2.ogMap
dist1 = og1.ogMap[i][0][1]
dist2 = og2.ogMap[i][0][1]
assert dist2 <= dist1
def testGeneralConstrainedBetterThanLeaves(self):
for tree in self.mcTrees:
og1 = GreedyOutgroup()
og1.importTree(tree)
candidates = set([tree.getName(x) for x in tree.getLeaves()])
og1.greedy(candidateSet=candidates, candidateChildFrac=2.)
og2 = GreedyOutgroup()
og2.importTree(tree)
og2.greedy(candidateSet=None, threshold=2)
for i in og1.ogMap:
assert i in og2.ogMap
dist1 = og1.ogMap[i][0][1]
dist2 = og2.ogMap[i][0][1]
assert dist2 <= dist1
def testMultipleOutgroups(self):
og = GreedyOutgroup()
og.importTree(self.borMcTree)
og.greedy(candidateChildFrac=0.5, maxNumOutgroups=3)
# make sure all entries have <= 3 outgroups.
assert all(map(lambda x: len(x) <= 3, og.ogMap.values()))
# and for all entries, the closest must be first.
assert all(
map(lambda x: x == sorted(x, key=itemgetter(1)),
og.ogMap.values()))
# ordering is important!
assert map(itemgetter(0), og.ogMap['Anc4']) == ['Anc1']
assert map(itemgetter(0),
og.ogMap['Anc7']) == ['BABOON', 'Anc1', 'Anc5']
# We avoid cycles, and choose post-order first, so this only
# uses leaves.
assert map(itemgetter(0),
og.ogMap['Anc1']) == ['HUMAN', 'CHIMP', 'BABOON']
def testMultipleOutgroupsJustLeaves(self):
og = GreedyOutgroup()
og.importTree(self.borMcTree)
candidates = set(
[self.borMcTree.getName(x) for x in self.borMcTree.getLeaves()])
og.greedy(candidateSet=candidates,
candidateChildFrac=2.,
maxNumOutgroups=3)
# make sure all entries have <= 3 outgroups.
assert all(map(lambda x: len(x) <= 3, og.ogMap.values()))
# and for all entries, the closest must be first.
assert all(
map(lambda x: x == sorted(x, key=itemgetter(1)),
og.ogMap.values()))
# ordering is important!
assert map(itemgetter(0),
og.ogMap['Anc1']) == ['HUMAN', 'CHIMP', 'BABOON']
assert og.ogMap['Anc7'][0][0] == 'BABOON'
assert og.ogMap['Anc7'][1][0] in ['CAT', 'DOG']
assert og.ogMap['Anc7'][2][0] in ['CAT', 'DOG']
def testMultipleOutgroupsOnRandomTrees(self):
for tree in self.mcTrees:
og = GreedyOutgroup()
og.importTree(tree)
og.greedy(candidateChildFrac=0.5, maxNumOutgroups=3)
# make sure all entries have <= 3 outgroups.
assert all(map(lambda x: len(x) <= 3, og.ogMap.values()))
# and for all entries, the closest must be first.
assert all(
map(lambda x: x == sorted(x, key=itemgetter(1)),
og.ogMap.values()))
def testDynamicOutgroupsOnRandomTrees(self):
for tree, seqMap in zip(self.mcTrees, self.dummySeqMaps):
degree = max([
len(tree.getChildren(x)) for x in tree.breadthFirstTraversal()
])
if degree < 8:
og = DynamicOutgroup()
og.edgeLen = 5
og.importTree(tree, seqMap)
og.compute(maxNumOutgroups=3)
# make sure all entries have <= 3 outgroups.
assert all(map(lambda x: len(x) <= 3, og.ogMap.values()))
# and for all entries, the closest must be first.
# (this will be true because all sequences are the same)
assert all(
map(lambda x: x == sorted(x, key=itemgetter(1)),
og.ogMap.values()))
def testDynamicOutgroupsJustLeaves(self):
og = DynamicOutgroup()
og.importTree(self.borMcTree, self.blanchetteSeqMap)
og.compute(maxNumOutgroups=3, sequenceLossWeight=0.)
# make sure all entries have <= 3 outgroups.
assert all(map(lambda x: len(x) <= 3, og.ogMap.values()))
# and for all entries, the closest must be first.
assert all(
map(lambda x: x == sorted(x, key=itemgetter(1)),
og.ogMap.values()))
# ordering is important!
assert og.ogMap['Anc1'][0][0] == 'HUMAN'
assert og.ogMap['Anc7'][0][0] == 'BABOON'
og = DynamicOutgroup()
og.importTree(self.borMcTree, self.blanchetteSeqMap)
og.compute(maxNumOutgroups=3)
# make sure all entries have <= 3 outgroups.
assert all(map(lambda x: len(x) <= 3, og.ogMap.values()))
# we keep dynamic outgroups sorted by distance too
assert all(
map(lambda x: x == sorted(x, key=itemgetter(1)),
og.ogMap.values()))
def testMultipleIdenticalRunsProduceSameResult(self):
"""The code now allows for multiple greedy() calls with different
candidate sets, so that some outgroups can be 'preferred' over
others without being the only candidates.
Check that running greedy() multiple times with the same
parameters gives the same result as running it once.
"""
for tree in self.mcTrees:
ogOnce = GreedyOutgroup()
ogOnce.importTree(tree)
ogOnce.greedy(maxNumOutgroups=3)
ogMultipleTimes = GreedyOutgroup()
ogMultipleTimes.importTree(tree)
ogMultipleTimes.greedy(maxNumOutgroups=3)
ogMultipleTimes.greedy(maxNumOutgroups=3)
ogMultipleTimes.greedy(maxNumOutgroups=3)
# make sure all entries have <= 3 outgroups.
assert all(
map(lambda x: len(x) <= 3, ogMultipleTimes.ogMap.values()))
# and for all entries, the closest must be first.
assert all(
map(lambda x: x == sorted(x, key=itemgetter(1)),
ogMultipleTimes.ogMap.values()))
# Check that the maps are equal. Can't compare them
# directly since python will convert them to ordered
# association lists.
assert len(ogOnce.ogMap) == len(ogMultipleTimes.ogMap)
for i in ogOnce.ogMap:
assert i in ogMultipleTimes.ogMap
assert ogOnce.ogMap[i] == ogMultipleTimes.ogMap[i]
def testPreferredCandidateSets(self):
"""Test that running greedy() multiple times with different candidate
sets will behave properly, i.e. keep all the existing outgroup
assignments and fill in more on the second run."""
for tree in self.mcTrees:
ogOnce = GreedyOutgroup()
ogOnce.importTree(tree)
nodes = [j for j in tree.postOrderTraversal()]
candidateSet = set([
tree.getName(i)
for i in random.sample(nodes, min(20, len(nodes)))
])
ogOnce.greedy(candidateSet=candidateSet, maxNumOutgroups=3)
ogTwice = GreedyOutgroup()
ogTwice.importTree(tree)
ogTwice.greedy(candidateSet=candidateSet, maxNumOutgroups=3)
ogTwice.greedy(maxNumOutgroups=3)
# make sure all entries have <= 3 outgroups.
assert all(map(lambda x: len(x) <= 3, ogTwice.ogMap.values()))
# and for all entries, the closest must be first.
assert all(
map(lambda x: x == sorted(x, key=itemgetter(1)),
ogTwice.ogMap.values()))
for node in ogTwice.ogMap:
if node in ogOnce.ogMap:
# the ogMap entry in ogOnce should be a subset of the ogMap entry for ogTwice
oneRunOutgroups = ogOnce.ogMap[node]
twoRunOutgroups = ogTwice.ogMap[node]
assert len(twoRunOutgroups) >= len(oneRunOutgroups)
for i in oneRunOutgroups:
assert i in twoRunOutgroups
def testNoOutgroupIsADescendantOfAnother(self):
"""No two outgroups should be on the same path to the root."""
for tree in self.mcTrees:
tree.nameUnlabeledInternalNodes()
og = GreedyOutgroup()
og.importTree(tree)
og.greedy(maxNumOutgroups=3)
for source in og.ogMap:
for (sink1, _) in og.ogMap[source]:
for (sink2, _) in og.ogMap[source]:
if sink1 != sink2:
sink1Id = tree.nameToId[sink1]
sink2Id = tree.nameToId[sink2]
assert sink1Id not in tree.postOrderTraversal(
sink2Id)
assert sink2Id not in tree.postOrderTraversal(
sink1Id)
def make_align_job(options, toil):
options.cactusDir = getTempDirectory()
# apply path overrides. this was necessary for wdl which doesn't take kindly to
# text files of local paths (ie seqfile). one way to fix would be to add support
# for s3 paths and force wdl to use it. a better way would be a more fundamental
# interface shift away from files of paths throughout all of cactus
if options.pathOverrides:
seqFile = SeqFile(options.seqFile)
configNode = ET.parse(options.configFile).getroot()
config = ConfigWrapper(configNode)
tree = MultiCactusTree(seqFile.tree)
tree.nameUnlabeledInternalNodes(
prefix=config.getDefaultInternalNodePrefix())
for name, override in zip(options.pathOverrideNames,
options.pathOverrides):
seqFile.pathMap[name] = override
override_seq = os.path.join(options.cactusDir, 'seqFile.override')
with open(override_seq, 'w') as out_sf:
out_sf.write(str(seqFile))
options.seqFile = override_seq
if not options.root:
seqFile = SeqFile(options.seqFile)
configNode = ET.parse(options.configFile).getroot()
config = ConfigWrapper(configNode)
mcTree = MultiCactusTree(seqFile.tree)
mcTree.nameUnlabeledInternalNodes(
prefix=config.getDefaultInternalNodePrefix())
options.root = mcTree.getRootName()
if options.acyclic:
seqFile = SeqFile(options.seqFile)
tree = MultiCactusTree(seqFile.tree)
leaves = [tree.getName(leaf) for leaf in tree.getLeaves()]
if options.acyclic not in leaves:
raise RuntimeError(
"Genome specified with --acyclic, {}, not found in tree leaves"
.format(options.acyclic))
#to be consistent with all-in-one cactus, we make sure the project
#isn't limiting itself to the subtree (todo: parameterize so root can
#be passed through from prepare to blast/align)
proj_options = copy.deepcopy(options)
proj_options.root = None
#Create the progressive cactus project (as we do in runCactusProgressive)
projWrapper = ProjectWrapper(proj_options,
proj_options.configFile,
ignoreSeqPaths=options.root)
projWrapper.writeXml()
pjPath = os.path.join(options.cactusDir, ProjectWrapper.alignmentDirName,
'%s_project.xml' % ProjectWrapper.alignmentDirName)
assert os.path.exists(pjPath)
project = MultiCactusProject()
if not os.path.isdir(options.cactusDir):
os.makedirs(options.cactusDir)
project.readXML(pjPath)
# open up the experiment (as we do in ProgressiveUp.run)
# note that we copy the path into the options here
experimentFile = project.expMap[options.root]
expXml = ET.parse(experimentFile).getroot()
experiment = ExperimentWrapper(expXml)
configPath = experiment.getConfigPath()
configXml = ET.parse(configPath).getroot()
seqIDMap = dict()
tree = MultiCactusTree(experiment.getTree()).extractSubTree(options.root)
leaves = [tree.getName(leaf) for leaf in tree.getLeaves()]
outgroups = experiment.getOutgroupGenomes()
genome_set = set(leaves + outgroups)
# this is a hack to allow specifying all the input on the command line, rather than using suffix lookups
def get_input_path(suffix=''):
base_path = options.cigarsFile[0]
for input_path in options.cigarsFile:
if suffix and input_path.endswith(suffix):
return input_path
if os.path.basename(base_path).startswith(
os.path.basename(input_path)):
base_path = input_path
return base_path + suffix
# import the outgroups
outgroupIDs = []
outgroup_fragment_found = False
for i, outgroup in enumerate(outgroups):
try:
outgroupID = toil.importFile(
makeURL(get_input_path('.og_fragment_{}'.format(i))))
outgroupIDs.append(outgroupID)
experiment.setSequenceID(outgroup, outgroupID)
outgroup_fragment_found = True
assert not options.pangenome
except:
# we assume that input is not coming from cactus blast, so we'll treat output
# sequences normally and not go looking for fragments
outgroupIDs = []
break
#import the sequences (that we need to align for the given event, ie leaves and outgroups)
for genome, seq in list(project.inputSequenceMap.items()):
if genome in leaves or (not outgroup_fragment_found
and genome in outgroups):
if os.path.isdir(seq):
tmpSeq = getTempFile()
catFiles(
[os.path.join(seq, subSeq) for subSeq in os.listdir(seq)],
tmpSeq)
seq = tmpSeq
seq = makeURL(seq)
logger.info("Importing {}".format(seq))
experiment.setSequenceID(genome, toil.importFile(seq))
if not outgroup_fragment_found:
outgroupIDs = [
experiment.getSequenceID(outgroup) for outgroup in outgroups
]
# write back the experiment, as CactusWorkflowArguments wants a path
experiment.writeXML(experimentFile)
#import cactus config
if options.configFile:
cactusConfigID = toil.importFile(makeURL(options.configFile))
else:
cactusConfigID = toil.importFile(makeURL(project.getConfigPath()))
project.setConfigID(cactusConfigID)
project.syncToFileStore(toil)
configNode = ET.parse(project.getConfigPath()).getroot()
configWrapper = ConfigWrapper(configNode)
configWrapper.substituteAllPredefinedConstantsWithLiterals()
if options.singleCopySpecies:
findRequiredNode(
configWrapper.xmlRoot,
"caf").attrib["alignmentFilter"] = "singleCopyEvent:{}".format(
options.singleCopySpecies)
if options.barMaskFilter:
findRequiredNode(
configWrapper.xmlRoot,
"bar").attrib["partialOrderAlignmentMaskFilter"] = str(
options.barMaskFilter)
if options.pangenome:
# turn off the megablock filter as it ruins non-all-to-all alignments
findRequiredNode(configWrapper.xmlRoot,
"caf").attrib["minimumBlockHomologySupport"] = "0"
findRequiredNode(
configWrapper.xmlRoot,
"caf").attrib["minimumBlockDegreeToCheckSupport"] = "9999999999"
# turn off mapq filtering
findRequiredNode(configWrapper.xmlRoot,
"caf").attrib["runMapQFiltering"] = "0"
# more iterations here helps quite a bit to reduce underalignment
findRequiredNode(configWrapper.xmlRoot,
"caf").attrib["maxRecoverableChainsIterations"] = "50"
# turn down minimum block degree to get a fat ancestor
findRequiredNode(configWrapper.xmlRoot,
"bar").attrib["minimumBlockDegree"] = "1"
# turn on POA
findRequiredNode(configWrapper.xmlRoot,
"bar").attrib["partialOrderAlignment"] = "1"
# save it
if not options.batch:
pg_file = options.outHal + ".pg-conf.xml"
if pg_file.startswith('s3://'):
pg_temp_file = getTempFile()
else:
pg_temp_file = pg_file
configWrapper.writeXML(pg_temp_file)
if pg_file.startswith('s3://'):
write_s3(pg_temp_file,
pg_file,
region=get_aws_region(options.jobStore))
logger.info("pangenome configuration overrides saved in {}".format(
pg_file))
workFlowArgs = CactusWorkflowArguments(options,
experimentFile=experimentFile,
configNode=configNode,
seqIDMap=project.inputSequenceIDMap)
#import the files that cactus-blast made
workFlowArgs.alignmentsID = toil.importFile(makeURL(get_input_path()))
workFlowArgs.secondaryAlignmentsID = None
if not options.pafInput:
try:
workFlowArgs.secondaryAlignmentsID = toil.importFile(
makeURL(get_input_path('.secondary')))
except:
pass
workFlowArgs.outgroupFragmentIDs = outgroupIDs
workFlowArgs.ingroupCoverageIDs = []
if outgroup_fragment_found and len(outgroups) > 0:
for i in range(len(leaves)):
workFlowArgs.ingroupCoverageIDs.append(
toil.importFile(
makeURL(get_input_path('.ig_coverage_{}'.format(i)))))
align_job = Job.wrapJobFn(run_cactus_align,
configWrapper,
workFlowArgs,
project,
checkpointInfo=options.checkpointInfo,
doRenaming=options.nonCactusInput,
pafInput=options.pafInput,
pafSecondaries=options.usePafSecondaries,
doVG=options.outVG,
doGFA=options.outGFA,
delay=options.stagger,
eventNameAsID=options.eventNameAsID,
acyclicEvent=options.acyclic)
return align_job